基于内容的月球遥感影像检索关键技术研究
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摘要
月球遥感影像由月球观测器利用遥感设备从近月空间获得，是月球探测的主要数据产品和进一步进行月球科学研究的重要依据。随着当前第二次月球探测热潮中各类探测计划的不断推进，积累了大量的月球遥感影像数据，如何高效检索从而充分利用这些宝贵的科学探测数据，已经成为月球科学研究新的关注点和研究热点之一。基于内容的月球遥感影像检索不同于传统的基于元数据的检索方式，无需提供专业、复杂、根据影像来源各不相同的查询参数作为检索条件，仅通过对比分析检索样例影像与影像库中月球影像的内容特征，即可得到多幅具有视觉相似性的库中影像作为检索结果。基于内容的检索方式对用户的领域知识要求低，能满足更广泛用户查找获取月球遥感影像的需求，从而使之得到更为充分的利用，发挥更大的科学效益，其研究具有重要的理论与实践意义。最近以来，这一方向已经引起了国内外学者的关注，开展了初步的研究并取得了一定的成果，但为取得更好的检索效果并与实际应用相契合，在其涉及的各关键技术点以及整体检索过程等方面均存在进一步探索的巨大空间。本文从实际需求出发，借鉴经典的基于内容的图像检索理论与技术，结合月球遥感影像的领域特点，从以下多个方面对基于内容的月球遥感影像检索开展了研究：
(1)月球遥感影像内容特征描述
根据月球遥感影像的内容特点，考虑影像包含目标撞击坑的视觉显著度，提出一种基于SURF的月球遥感影像显著区域提取算法，算法通过合并组合明暗SURF局部特征获得候选ROI，通过SVM对ROI进行分类识别得到影像显著区域集合。在此基础上，考虑多个显著区域之间的相对位置和强度比尺度比等关系，提出了一种LIFBS特征，并给出其经过多线程并行优化的详细特征计算步骤。实验结果表明，本文研究的显著区域提取算法能够准确地提取出月球影像中绝大部分显著撞击坑区域，检测效果明显优于Itti经典显著区检测模型；LIFBS特征能较好地描述月球影像内容，具有良好的不变性和相似性，优于灰度直方图、HU形状矩、Tamura纹理等经典图像全局内容特征。
(2)特征向量高维索引
面向全局特征向量近邻查询，提出一种支持并发查询的精确高维索引结构PCVAH，PCVAH采用近似向量空间上的哈希结构组织数据，利用近邻模版加速过滤，基于多核多线程技术支持多个并发查询的并行处理。在PCVAH基础上，提出一种近似近邻索引结构PQH，PQH通过分割查询空间、简化过滤过程进一步提高全局特征近邻查询的效率。面向高维局部特征的查询匹配（2近邻）问题，提出一种PCPF索引，使用k-D树结构组织查询空间，通过近似向量减少磁盘I/O，基于优先队列和限制回溯节点总数进一步提高效率，通过分割查询空间支持多线程并行处理。实验结果表明，三种索引均取得了较好的查询检索性能，分别优于VA+-file、LSH、BBF等经典索引方法，并取得了较好的并行效率；对于近似索引，PQH得到的查全率与查准率与精确查找相当，PCPF匹配效果优于BBF算法。
(3)基于内容的月球遥感影像检索过程。
结合特征描述与高维索引，提出月球遥感影像复合内容特征模型和相似性度量方法，在此基础上提出整体的相似性检索算法。详细分析了整体检索过程并基于Petri网进行过程建模，在模型基础上进行了多节点多核环境下的并行设计，提出一种月球遥感影像内容检索并行框架。实验结果表明，通过本文所提的月球遥感影像检索方法，能够得到与查询样例影像具有视觉相似性的库中影像集合；提出的并行框架能够有效提升整体的查询检索效率。
(4)基于内容的月球遥感影像检索原型系统设计与实现。
基于月球空间数据库，设计实现了一个基于内容的月球遥感影像检索原型系统。一方面，验证了本文提出方法的有效性和实用性；另一方面，展示了这种全新的检索方式在月球探测数据分发利用中的潜在应用。在需求分析的基础上，设计了系统功能、系统逻辑结构和系统物理部署。展示了通过原型系统进行基于内容的月球遥感影像检索的应用实例。
Lunar remote sensing images are shot by the sensor equipments from the spacenear the moon surface, and they are the main data products and foundations for furthermoon studies. With the promotion of different missions in the current second lunarexploration boom, large amounts of lunar remote sensing images are accumulated. Howto retrieval these valuable data efficiently and thus make the best of them becomes oneof the new research hotspots. Content-based lunar remote sensing image retrieval,different from the traditional retrieval method based on metadata, no longer needs toprovide the complex and expertized different parameters as the retrieval input. Instead,several similar images in the lunar image database can be found as the retrieval resultsonly by analyzing and comparing the visual content of a query sample and images indatabase. It reduces the requirement for domain knowledge of users and makes theimage data available to more extensive users, which enables the fully use of lunarremote sensing images and brings out more scientific benefits. The research ofcontent-based lunar remote sensing image retrieval is significant both in theory and inpractice, which has caught the attention of researchers recently. Some efforts havealready been made on it globally, however, both the referring key techniques and thewhole retrieval method requires more improvements. Aiming at practical applications,this dissertation studies the problem of content-based lunar remote sensing imageretrieval by taking the idea of classical CBIR theory and techniques together withconsidering the domain specialty of lunar remote sensing images. The maincontributions of this dissertation include:
(1) The problem of content features of lunar remote sensing images
According to the specific visual appearance of lunar remote sensing images,considering the saliency of the image-contained craters, a salient region detectionalgorithm is proposed for lunar images. It merges and combines the highlight and darkSURF points to generate candidate ROIs, and through a SVM classifier the less salientor falsely detected ROIs are excluded from the final detection results. On the basis ofthe salient region detection, a kind of feature descriptors called LIFBS is proposed.LIFBS are calculated according to the comparative position, strength and scale relationsamong detected salient regions. The LIFBS generating algorithm is designed to supportmulti-threaded parallelism and is described in detail. Experimental results show that: theproposed detection algorithm is able to pick out most of the salient crater regions in alunar remote sensing image and works much better than the classical ITTi’s model. TheLIFBS describes the content of a lunar image well and is of good invariance andsimilarity. It works better than some classical global features such as grayscalehistogram, HU moments and Tamura texture descriptors.
(2) The problem of high-dimensional indexes for feature vectors
Facing the NN search of global feature vectors, an exact high-dimensional indexsupporting concurrent queries called PCVAH is proposed. It uses hash-style structure toorganize the approximate vectors, filters the candidates by neighboring masks andsupports concurrent queries basing on multi-threaded parallelism. On the basis ofPCVAH, an approximate index called PQH is proposed. It partitions the query spaceand simplifies the filtering, which help to improve the NN search efficiency of globalfeature vectors. A PCPF index is proposed for matching the local feature vector (equalsto a2-NN problem) more efficiently. It uses a k-D tree to organize the query space,reduces the I/O by vector approximation and saves more time by a priority queue andthe restriction of the tracing nodes number. Experimental results show that: theproposed three indexes are of high query efficiency and outperform the classicalVA+-file, LSH and BBF index methods separately. For approximate methods, the queryprecision and recall of PQH is similar as that of exact search, while the matching resultby PCPF is better than that by BBF method.
(3) The overall content-based lunar remote sensing image retrieval method
A compound feature model and a similarity measurement are proposed combiningthe contributions of the studies of feature descriptors and high-dimensional indexes forlunar remote sensing images. Basing on the feature model and the similaritymeasurement, the overall algorithm for similarity retrieval is proposed. After that, theoverall retrieval process is analyzed in detail and a Petri net tool is used to construct theprocess model. For parallel design in conditions of clusters and multi-cores, a parallelframework for content-based lunar remote sensing image retrieval is proposed byanalyzing the Petri net process model. Experimental results show that: the proposedmethod for retrieving lunar remote sensing images by the content is able to offervisually similar images in the database as retrieval results and the parallel frameworkhelps to speedup the overall process of retrieval.
(4) The design and implemention of a prototype content-based lunar remotesensing image retrieval system
Basing on the lunar image database, a prototype system for content-based lunarremote sensing image retrieval is designed and implemented. On one hand, it verifiesthe effectiveness and feasibleness of the proposed algorithms. On the other hand, itdemonstrates the potential applications of the new retrieval method in the distributionand using of lunar exploratory data. The function, logical architecture and physicaldeployment of the prototype system are designed according to the analysis ofrequirements. Finally, the application of content-based lunar remote sensing imageretrieval is demonstrated via the prototype system.
(1)月球遥感影像内容特征描述
根据月球遥感影像的内容特点，考虑影像包含目标撞击坑的视觉显著度，提出一种基于SURF的月球遥感影像显著区域提取算法，算法通过合并组合明暗SURF局部特征获得候选ROI，通过SVM对ROI进行分类识别得到影像显著区域集合。在此基础上，考虑多个显著区域之间的相对位置和强度比尺度比等关系，提出了一种LIFBS特征，并给出其经过多线程并行优化的详细特征计算步骤。实验结果表明，本文研究的显著区域提取算法能够准确地提取出月球影像中绝大部分显著撞击坑区域，检测效果明显优于Itti经典显著区检测模型；LIFBS特征能较好地描述月球影像内容，具有良好的不变性和相似性，优于灰度直方图、HU形状矩、Tamura纹理等经典图像全局内容特征。
(2)特征向量高维索引
面向全局特征向量近邻查询，提出一种支持并发查询的精确高维索引结构PCVAH，PCVAH采用近似向量空间上的哈希结构组织数据，利用近邻模版加速过滤，基于多核多线程技术支持多个并发查询的并行处理。在PCVAH基础上，提出一种近似近邻索引结构PQH，PQH通过分割查询空间、简化过滤过程进一步提高全局特征近邻查询的效率。面向高维局部特征的查询匹配（2近邻）问题，提出一种PCPF索引，使用k-D树结构组织查询空间，通过近似向量减少磁盘I/O，基于优先队列和限制回溯节点总数进一步提高效率，通过分割查询空间支持多线程并行处理。实验结果表明，三种索引均取得了较好的查询检索性能，分别优于VA+-file、LSH、BBF等经典索引方法，并取得了较好的并行效率；对于近似索引，PQH得到的查全率与查准率与精确查找相当，PCPF匹配效果优于BBF算法。
(3)基于内容的月球遥感影像检索过程。
结合特征描述与高维索引，提出月球遥感影像复合内容特征模型和相似性度量方法，在此基础上提出整体的相似性检索算法。详细分析了整体检索过程并基于Petri网进行过程建模，在模型基础上进行了多节点多核环境下的并行设计，提出一种月球遥感影像内容检索并行框架。实验结果表明，通过本文所提的月球遥感影像检索方法，能够得到与查询样例影像具有视觉相似性的库中影像集合；提出的并行框架能够有效提升整体的查询检索效率。
(4)基于内容的月球遥感影像检索原型系统设计与实现。
基于月球空间数据库，设计实现了一个基于内容的月球遥感影像检索原型系统。一方面，验证了本文提出方法的有效性和实用性；另一方面，展示了这种全新的检索方式在月球探测数据分发利用中的潜在应用。在需求分析的基础上，设计了系统功能、系统逻辑结构和系统物理部署。展示了通过原型系统进行基于内容的月球遥感影像检索的应用实例。
Lunar remote sensing images are shot by the sensor equipments from the spacenear the moon surface, and they are the main data products and foundations for furthermoon studies. With the promotion of different missions in the current second lunarexploration boom, large amounts of lunar remote sensing images are accumulated. Howto retrieval these valuable data efficiently and thus make the best of them becomes oneof the new research hotspots. Content-based lunar remote sensing image retrieval,different from the traditional retrieval method based on metadata, no longer needs toprovide the complex and expertized different parameters as the retrieval input. Instead,several similar images in the lunar image database can be found as the retrieval resultsonly by analyzing and comparing the visual content of a query sample and images indatabase. It reduces the requirement for domain knowledge of users and makes theimage data available to more extensive users, which enables the fully use of lunarremote sensing images and brings out more scientific benefits. The research ofcontent-based lunar remote sensing image retrieval is significant both in theory and inpractice, which has caught the attention of researchers recently. Some efforts havealready been made on it globally, however, both the referring key techniques and thewhole retrieval method requires more improvements. Aiming at practical applications,this dissertation studies the problem of content-based lunar remote sensing imageretrieval by taking the idea of classical CBIR theory and techniques together withconsidering the domain specialty of lunar remote sensing images. The maincontributions of this dissertation include:
(1) The problem of content features of lunar remote sensing images
According to the specific visual appearance of lunar remote sensing images,considering the saliency of the image-contained craters, a salient region detectionalgorithm is proposed for lunar images. It merges and combines the highlight and darkSURF points to generate candidate ROIs, and through a SVM classifier the less salientor falsely detected ROIs are excluded from the final detection results. On the basis ofthe salient region detection, a kind of feature descriptors called LIFBS is proposed.LIFBS are calculated according to the comparative position, strength and scale relationsamong detected salient regions. The LIFBS generating algorithm is designed to supportmulti-threaded parallelism and is described in detail. Experimental results show that: theproposed detection algorithm is able to pick out most of the salient crater regions in alunar remote sensing image and works much better than the classical ITTi’s model. TheLIFBS describes the content of a lunar image well and is of good invariance andsimilarity. It works better than some classical global features such as grayscalehistogram, HU moments and Tamura texture descriptors.
(2) The problem of high-dimensional indexes for feature vectors
Facing the NN search of global feature vectors, an exact high-dimensional indexsupporting concurrent queries called PCVAH is proposed. It uses hash-style structure toorganize the approximate vectors, filters the candidates by neighboring masks andsupports concurrent queries basing on multi-threaded parallelism. On the basis ofPCVAH, an approximate index called PQH is proposed. It partitions the query spaceand simplifies the filtering, which help to improve the NN search efficiency of globalfeature vectors. A PCPF index is proposed for matching the local feature vector (equalsto a2-NN problem) more efficiently. It uses a k-D tree to organize the query space,reduces the I/O by vector approximation and saves more time by a priority queue andthe restriction of the tracing nodes number. Experimental results show that: theproposed three indexes are of high query efficiency and outperform the classicalVA+-file, LSH and BBF index methods separately. For approximate methods, the queryprecision and recall of PQH is similar as that of exact search, while the matching resultby PCPF is better than that by BBF method.
(3) The overall content-based lunar remote sensing image retrieval method
A compound feature model and a similarity measurement are proposed combiningthe contributions of the studies of feature descriptors and high-dimensional indexes forlunar remote sensing images. Basing on the feature model and the similaritymeasurement, the overall algorithm for similarity retrieval is proposed. After that, theoverall retrieval process is analyzed in detail and a Petri net tool is used to construct theprocess model. For parallel design in conditions of clusters and multi-cores, a parallelframework for content-based lunar remote sensing image retrieval is proposed byanalyzing the Petri net process model. Experimental results show that: the proposedmethod for retrieving lunar remote sensing images by the content is able to offervisually similar images in the database as retrieval results and the parallel frameworkhelps to speedup the overall process of retrieval.
(4) The design and implemention of a prototype content-based lunar remotesensing image retrieval system
Basing on the lunar image database, a prototype system for content-based lunarremote sensing image retrieval is designed and implemented. On one hand, it verifiesthe effectiveness and feasibleness of the proposed algorithms. On the other hand, itdemonstrates the potential applications of the new retrieval method in the distributionand using of lunar exploratory data. The function, logical architecture and physicaldeployment of the prototype system are designed according to the analysis ofrequirements. Finally, the application of content-based lunar remote sensing imageretrieval is demonstrated via the prototype system.
引文
[1]欧阳自远,李春来,邹永廖等.绕月探测工程的初步科学成果[J].中国科学:地球科学,2010,40(3):261~280.
[2] NASA. CLEMENTINE Website [OL]. http://nssdc.gsfc.nasa.gov/planetary/clementine.html.
[3] NASA. LRO Website [OL]. http://www.nasa.gov/lro/.
[4] ESA. SMART-1Website [OL]. http://www.esa.int/esaMI/SMART-1/index.html.
[5] JAXA. SELENE Website [OL]. http://www.kaguya.jaxa.jp.
[6] ISRO. Chandrayaan-1Website [OL]. http://www.isro.org/chandrayaan/.
[7]欧阳自远.我国月球探测的总体科学目标与发展战略[J].地球科学进展,2004,19(3):351~358.
[8]中科院国家天文台.嫦娥月球数据发布网站[OL]. http://moon.bao.ac.cn.
[9] Smeulders A.W.M, Worring M, Santini S, et al. Content-Based ImageRetrieval at the End of the Early Years [J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,2000,22(12):1349~1380.
[10]章毓晋.基于内容的视觉信息检索[M].北京:科学出版社,2003.
[11]周明全,耿国华,韦娜.基于内容图像检索技术[M].北京:清华大学出版社,2007.
[12]马修军.多媒体数据库与内容检索[M].北京:北京大学出版社,2007.
[13] Google [OL]. http://www.google.com.
[14] Bing [OL]. http://www.bing.com.
[15]百度识图[OL]. http://stu.baidu.com/.
[16] Montoya Zegarra J, Leite N, Silva Torres R. Wavelet-based FingerprintImage Retrieval [J]. Journal of Computational and Applied Mathematics,2009,227(2):294~307.
[17] Cappelli R, Ferrara M. A Fingerprint Retrieval System Based on Level-1andLevel-2Features [J]. Expert Systems with Applications,2012,39(12):10465~10478.
[18] Gao Y, Qi Y. Robust Visual Similarity Retrieval in Single Model FaceDatabases [J]. Pattern Recognition,2005,38(7):1009~1020.
[19] Park U, Jain A. Face Matching and Retrieval Using Soft Biometrics [J]. IEEETransactions on Information Forensics and Security,2010,5(3):406~415.
[20] Gold M, Thies C, Fischer B. A Generic Concept for the Implementation ofMedical Image RetrievalSystems [J]. International Journal of Medical Informatics,2007,76(2-3):252~259.
[21] Rahman M, Desai B, Bhattacharya P. Medical Image Retrieval withProbabilistic Multi-class Support Vector Machine Classifiers and Adaptive SimilarityFusion [J]. Computerized Medical Imaging and Graphics,2008,32(2):95~108.
[22] Kato T. Database Architecture for Content-based Image Retrieval [C]//Proceedings of SPIE on Image Storage and Retrieval Systems,1992,1662:112~123.
[23] Datta R, Joshi D, Li J, et al. Image Retrieval: Ideas, Influences, and Trends ofthe New Age [J], ACM Computing Surveys,2008,40(2):5:1~5:60.
[24] Flickner M, Sawhney H, Niblack w, et a1. Query by Image and VideoContent: the QBIC System [J]. IEEE Computer,1995,28(9):23~32.
[25] Niblack w, Zhu X, Hafner J, et al. Updates to the QBIC System [J]. SPIE,1997,3312:150~161.
[26] Gupta A, Jain R. Visual Information Retrieval [J]. Communications of ACM,1997,40(5):70~79.
[27] Bach J, Fuller C, Gupta A, et al. The Virage Image Search Engine: An OpenFamework for Image Management [C]//Proceedings of SPIE Storage and Retrieval forImage and Video Database,1996,2670:76~87.
[28] Pentland A, Picard R, Sclaroff S. Photobook: Tools for Content-basedManipulation of Image Database [C]//Proceedings of SPIE Storage and Retrieval forImage and Video Database,1994,2185:34~47.
[29] Smith J, Chang S. VisualSEEK: A Fully Automated Content-based ImageQuery System [C]//Proceedings of ACM Multimedia,1996,87~98.
[30] Smith J, Chang S. Visually Searching the Web for Content [J]. IEEEMultimedia Magazine,1997,4(3):12~20.
[31] Huang T, Mehrotra S, Ranlchandran K. Multimedia Analysis and RetrievalSystem (MARS) Project [C]//Proceedings of33th Annual Clinic on Library Applicationof Data Processing Digital Image Access and Retrieval,1996,101~117..
[32] Rui Y, Huang T, Ortega M, et al. Relevance Feedback: A Power Tool forInteractive Content-Based Image Retrieval [J]. IEEE Transactions on Circuits andSystems for Video Technology,1998,8(5):644~655.
[33] TinEye [OL]. http://tineye.com/.
[34]白雪生,廖春元,徐光佑,史元春. ImgRetr—一个基于内容的图像检索系统[C].第七届全国多媒体技术学术年会,1998.
[35] Jing F, Zhang B, Lin F, et al. A Novel Region-Based Image Retrieval MethodUsing Relevance Feedback [C]//Proceedings of the3rd International ACM Workshopon Multimedia Information Retrieval,2001,28~31.
[36]朱兴全,张宏江等. iFind:一个结合语义和视觉特征的图像相关反馈检索系统[J].计算机学报,2002,7:681~689.
[37]李鹏杰,杨树元.一种基于内容的图像检索系统lmageHunter[J].微计算机应用,2001,5:139~141.
[38]中国科学院成果鉴定报告.基于特征的多媒体信息检索系统MIRES（国家863高技术计划通信技术主题资助项目）1999,12,15.
[39]袁昕,朱淼良,王东辉.基于图像主色特征的CBIR[J].计算机辅助设计与图形学学报,2002,14(11):1061~1065.
[40]袁昕,朱淼良.基于主色匹配的图像检索系统[J].计算机辅助设计与图形学学报,2000,12,917~921.
[41]孙君顶,基于内容的图像检索技术研究[D].博士论文.西安:西安电子科技大学.2005.
[42]赵珊,基于内容的图像检索关键技术研究[D].博士论文.西安:西安电子科技大学.2007.
[43]安志勇,基于内容的图像检索关键技术研究[D].博士论文.西安:西安电子科技大学.2008.
[44]周向东,图像数据库检索中的关键技术研究[D].博士论文.上海:复旦大学.2003.
[45]张静,视频信息检索研究[D].博士论文.上海:复旦大学.2006.
[46]沈项军,基于语义学习的图像检索研究[D].博士论文.合肥:中国科学技术大学.2006.
[47]李杰,基于内容的图像检索方法研究[D].博士论文.合肥:中国科学技术大学.2008.
[48]梁栋,基于内容的图像检索中若干关键问题研究[D].博士论文.上海:上海交通大学.2005.
[49]韦娜,基于内容图像检索关键技术研究[D].博士论文.西安:西北大学.2006.
[50]夏定元,基于内容的图像检索通用技术研究及应用[D].博士论文.武汉:华中科技大学.2004.
[51] Kelly P, Cannon M, Hush D. Query by Image Example: the CANDIDApproach [C]//Proceedings of SPIE,1995,2420:238~248.
[52] Kelly P, Cannon M. CANDID: Comparison Algorithm for Navigating DigitalImage Databases [C]//Proceedings of the7th International Working Conference onScientific and Statistical Database Management,1994,252~258.
[53] Ma W, Manjunath B. Netra: A Toolbox for Navigating Large ImageDatabases [J]. Multimedia System,1999,17(3): l84~198.
[54] UCSB. Alexandria Website [OL]. http://www.alexandria.ucsb.edu/.
[55] Schroder M, Rehrauer H, Seidel K, et al. Interactive Learning andProbabilistic Retrieval in Remote Sensing Image Archives [J]. IEEE Transactions onGeoscience and Remote Sensing,2000,38(5):100~119.
[56] Seidel K, Schroder M, Rehrauer H, et al. Query by Image Content fromRemote Sensing Archives [C]//Proceedings of IGARSS,1998,1:393~396.
[57] Bretschneider T.(RS)2I-Retrieval System for Remotely Sensed ImageryOn-board Image Processing for the NTU Satellite X-Sat [EB/OL]. DOI:10356/2313.
[58] Bretschneider T, Kao O. A Retrieval System for Remotely Sensed I magery[C]//Proceedings of the International Conference on Imaging Science, Systems, andTechnology,2002,2:439~445.
[59] Carson C, Thomas M, Belongie S, et al. Blobworld: a System forRegion-Based Image Indexing and Retrieval [C]//Proceedings of InternationalConference on Visual Information Systems,1999,660~672.
[60] Datcu M, Daschiel H, Pelizzari A, et al. Information Mining in RemoteSensing Image Archives: System Concepts [J]. IEEE Transactions on Geoscience andRemote Sensing,2003,41(12):2923~2936.
[61] Shyu C-R, Klaric M, Scott G, er al. GeoIRIS: Geospatial InformationRetrieval and Indexing System—Content Mining, Semantics Modeling, and ComplexQueries [J]. IEEE Transactions on Geoscience and Remote Sensing,2007,45(4):839~852.
[62]周焰,李德仁,徐长勇.基于形状的遥感图像检索系统[J].华中科技大学学报自然科学版,2003,31(3):14~16.
[63]程起敏.基于内容的遥感影像库检索关键技术研究[D].博士论文.北京:中国科学院遥感应用研究所.2004.
[64]李锋,曾志明,付琨,吴一戎.遥感影像数据库基于内容检索系统的构建[J].武汉大学学报信息科学版,2005,30(9):787~790.
[65]曾志明.基于内容检索的遥感影像数据库系统研究[D].硕士论文.北京:中国科学院电子学研究所.2005.
[66]李嘉.基于内容的多光谱遥感影像检索若干关键技术研究[D].博士论文.武汉:华中科技大学.2009.
[67]杜培军.高光谱遥感影像检索理论与方法的研究[D].博士后出站报告.上海:上海交通大学.2004.
[68] Meyer C, Deans M. Content Based Retrieval of Images for PlanetaryExploration [C]//Proceedings of the2007IEEE/RSJ International Conference onIntelligent Robots and Systems,2007:1377~1382.
[69] NASA. Apollo Website [OL]. http://www.nasa.gov/nssdc.nasa.gov/planetary/lunar/apollo.html.
[70] NASA. Mars Global Surveyor Mars Orbiter Camera Image Gallery [OL].http://www.msss.com/moc_gallery/.
[71] NASA. Mars Exploration Rover Mission Home [OL]. http://marsrovers.jpl.nasa.gov/home/index.html.
[72] Cheng H-T, Sun F-T, Buthpitiya S. Lunar Image Classification for TerrainDetection [J]. Lecture Notes on Computer Science2010,6455:1~8.
[73] Cover T, Hart P. Nearest Neighbor Pattern Classification [J]. IEEETransactions on Information Theory,1967,13(1):21~27.
[74] Quinlan J R. Introduction of Decision Tree [J]. Machine Learning,1986,1(1):81~109.
[75] Cheng H-T, Sun F-T, Buthpitiya S, et al. Lunar Image Labeling Tool [OL].http://www.ece.cmu.edu/~hengtzec/projects/lunar/LILT.zip
[76]李珂,陈建平.嫦娥数据管理系统设计与实现[J].计算机工程,2012,38(4):218~220.
[77]李莎莎.月球科学数据编目处理与集成管理技术研究[D].硕士论文.长沙:国防科学技术大学.2010.
[78]余盼盼.月球空间数据共享发布技术研究[D].硕士论文.长沙:国防科学技术大学.2010.
[79]刘实.全局与局部特征相结合的遥感影像检索方法研究[D].硕士论文.长沙:国防科学技术大学.2010.
[80]刘潺.月球探测空间数据集成与检索技术研究[D].硕士论文.长沙:国防科学技术大学.2011.
[81] Swain M J, Ballard D H. Color Indexing [J]. International Journal ofComputer Vision,1991,7(1):11~32.
[82] Gong Y, Zhang H J, Chua T C. An Image Database System with ContentCapturing and Fast Image Indexing Abilities [C]//Proceedings of IEEE InternationalConference on Multimedia Computing and Systems,1994,121~130.
[83] Stricker M, Orengo M. Similarity of Color Images [C]//Proceedings of SPIEStorage and Retrieval for Image and Video Databases,1995,2185:381~3925.
[84] Smith J R, Chang S-F. Tools and Techniques for Color Image Retrieval[C]//Proceedings of SPIE Storage and Retrieval for Image and Video Databases,1995,2670:426~437.
[85] Pass G, Zabih R. Histogram Refinement for Content-based Image Retrieval
[C]//Proceedings of IEEE Workshop on Applications of Computer Vision,1996,96~102.
[86] Huang J, et al. Image Indexing Using Color Correlogram [C]//Proceedings ofIEEE International Conference on Computer Vision and Pattern Recognition,1997,762~768.
[87] Tamura H, Mori S, Yamawaki T. Texture Features Corresponding to VisualPerception [J]. IEEE Transactions on Systems, Man, and Cybernetics,1978,8(6):460~473.
[88] Niblack et al. The QBIC Project: Querying Images by Content Using Color,Texture, and Shape [C]//Proceedings of SPIE, Storage and Retrieval for Image andVideo Databases,1993,1908:173~187.
[89] Mao J, Jain A K. Texture Classification and Segmentation UsingMultiresolution Simultaneous Autoregressive Models [J]. Pattern Recognition,1992,25(2):173~188.
[90] Li S Z. Markov Random Field Modeling in Image Analysis [M]. Dordrecht:Springer Press,2009.
[91] Chang T, Jay Kuo C. Texture Analysis and Classification withTree-structured Wavelet Transform [J]. IEEE Transactions on Image Processing,1993,2(4):429~441.
[92] Laine A, Fan J. Texture Classification by Wavelet Packet Signatures [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(11):1186~1191.
[93] Robert M. Haralick K. Shanmugam D. Texture Features for ImageClassification [J]. IEEE Transactions on Systems, Man, and Cybernetics,1973,3(6):610~621.
[94] Daugman J G. Complete Discrete2D Gabor Transforms by Neural Networksfor Image Analysis and Compression [J]. IEEE Transactions on ASSP,1998,36:1169~1179.
[95] Bovic A, Clark M, Geisler W. Multichannel Texture Analysis UsingLocalized Spatial Filters [J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,1990,12:55~73.
[96] Gross M, Koch R, Lippert Li, et al. Multiscale Image Texture Analysis inWavelet Spaces [C]//Proceedings of IEEE International Conference on ImageProcessing,1994,3:412~416.
[97] Thyagarajan K, Nguyen T, Persons C. A Maximum Likelihood Approach toTexture Classification Using Wavelet Transform [C]//Proceedings of IEEE InternationalConference on Image Processing,1994,2:640~644.
[98] Hu M K. Visual Pattern Recognition by Moment Invariants [J]. IRETransactions on Information Theory,1962,8:179~187.
[99] Chen C C. Improved Moment Invariants for Shape Discrimination [J]. PatternRecognition,1993,26(5):683~686.
[100]庄越挺.智能多媒体信息分析与检索的研究[D].博士论文.杭州:浙江大学.1998.
[101] Zhang D, Image Retrieval Based on Shape [D]. PhD Thesis, MonashUniversity,2002.
[102] Pentland A, Picard R, Sclaroff S. Photobook: Comtent-based Manipulationof Image Databases [J]. International Journal of Computer Vision,1996,18(3):233~254.
[103] Esther M, Arkin L, Chew D, et al. An Efficienly Computable Metric forComparing Polygonal Shapes [J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,1991,13(3):209~216.
[104] Gene C, Chuang H, and Jay C. Wavelet descriptor of Planar Curves: Theoryand Applications [J]. IEEE Transactions on Image Processing,1996,5(1):56~70.
[105] Huang W, Gao Y, Chan K. A Review of Region-Based Image Retrieval [J].Journal of Signal Processing System,2010,59:143~161.
[106] Almeida J, Rocha A, Torres R, et al. Making Colors Worth More Than aThousand Words [C]//Proceedings of the ACM Symposium on Applied Computing,2008,1180~1186.
[107] Sun Y, Ozawa S. A Hierarchical Approach for Region-based ImageRetrieval.[C]//Proceedings of the IEEE Conference on Computer Vision and PatternRecognition,2004,2:996~1001.
[108] Chen Y, Wang J. A Region-based Fuzzy Feature Matching Approach toContent-based Image Retrieval [J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,2002,24:1252~1267.
[109] Xiong W, Qiu B, Tian Q, et al. MultiPRE: A Novel Framework withMultiple Parallel Retrieval Engines for Content-based Image Retrieval [C]//Proceedingsof the ACM International Ionference on Multimedia,2005,1023~1032.
[110] Harris C, Stephens M.1988. A Combined Corner and Edge Detector.[C]//Proceedings of Alvey Vision Conference,1988,147~151.
[111] Smith S M, Brady J M. SUSAN-A New Approach to Low Level ImageProcessing [J]. Journal of Computer Vision,1997,23(1):45~78.
[112] Mikolajczyk K, Schmid C. Scale&Affine Invariant Interest Point Detectors[J]. International Journal of Computer Vision,2004,60(1):63~86.
[113] Mikolajczyk K, Schmid C. Indexing Based on Scale Invariant InterestPoints [C]//Proceedings of ICCV,2001,525~531.
[114] Lowe D. Distinctive Image Features from Scale Invariant Keypoints [J].International Journal of Computer Vision,2004,60(2):92~110.
[115] Ke Y, Sukthankar R. PCA-SIFT:“A More Distinctive Representation forLocal Image Descriptors”[C]//Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition,2004,511~517.
[116] Mikolajczyk K, Tuytelaars T, Schmid C, et al. A Comparison of AffineRegion Detectors [J]. International Journal of Computer Vision,2005,65:43~72.
[117] Bay S H, Tuytelaars T, Gool L V. Surf: Speeded Up Robust Features [C]//Proceedings of European Conference on Computer Vision,2006:404~417.
[118] HE J, LI M, ZHANG H-J, et al. Manifold-ranking Based Image Retrieval[C]//Proceedings of the ACM International Conference on Multimedia,2004,9~16.
[119] Vasconcelos N, Lippman A. A Multiresolution Manifold Distance forInvariant Image Similarity [J]. IEEE Transactions on Multimedia,2005,7(1):127~142.
[120] Kullback S, Burnham K P, Laubscher N F. The Kullback-Leibler Distance[J]. The American Statistician,1987,41(4):340~344.
[121] Do M N. Vetterli M. Wavelet-Based Texture Retrieval Using GeneralizedGaussian Density and Kullback-Leibler Distance [J]. IEEE Transactions on ImageProcessing.2002,11(2):146~158.
[122] Jing F, Li M-J, Zhang H-J, et al. An Efficient and Effective Region-basedImage Retrieval Framework [J]. IEEE Transactions on Image Processing,2004,13(5):699~709.
[123] Wang J, Li J, Wiederhold G. Simplicity: Semantics-Sensitive IntegratedMatching for Picture Libraries [J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,2001,23(9):947~963.
[124] Rubner Y, Tomasi C, Guibas L. The Earth Mover’s Distance as a Metric forImage Retrieval [J]. International Journal of Computer Vision,2000,40(2),99~121.
[125]董道国,高维数据索引结构研究[D].博士论文.上海:复旦大学.2005.
[126] Guttman A. R-tree: A Dynamic Index Structure for Spatial Searching [C]//Proceedings of SIGMOD,1984,47~57.
[127] Sellis T, Roussopoulos N, Faloutsos C. The R+-Tree: A Dynamic Index forMulti-Dimensional Objects [C]//Proceedings of VLDB,1987,507~518.
[128] Beckmann N, Kriegel H-P, Schneider R, et al. The R*-tree: An Efficient andRobust Access Method for Points and Rectangles [C]//Proceedings of SIGMOD,1990,322~331.
[129] Berchtold S, Keim D, Kriegel H-P. The X-tree: An Index Structure forHigh-dimensional Data [C]//Proceedings of VLDB,1996,28~39.
[130] White D, Jain R. Similarity Indexing with the SS-tree [C]//Proceedings ofICDE,1996,516~523.
[131] Katayama N, Satoh S. The SR-tree: An Index Structure forHigh-dimensional Nearest Neighbor Queries [C]//Proceedings of SIGMOD,1997,369~380.
[132] Weber R, Schek H, Blott S. A Quantitative Analysis and Performance Studyfor Similarity-Search Methods in High-Dimensional Spaces [C]//Proceedings of VLDB,1998,194~205.
[133] Ferhatosmanoglu H, Tuncel E, Agrawal D, et al. Approximate NearestNeighbor Searching in Multimedia Databases [C]//Proceedings of ICDE,2001,503~511.
[134] Berchtold S, Bohm C, Jagadish H, et al. Independent quantization: An IndexCompression Technique for High-dimensional Data Spaces [C]//Proceedings of ICDE,2000,577~588.
[135] Cha G, Zhu X, Petkovic D, et al. An Efficient Indexing Method for NearestNeighbor Searches in High-Dimensional Image Databases [J]. IEEE Transactions onMultimedia,2002,4(1):76~87.
[136] Sakurai Y, Yoshikawa M, Uemura S, et al. The A-tree: An Index Structurefor High-dimensional Spaces Using Relative Approximation [C]//Proceedings of VLDB,2000,516~526.
[137] Heisterkamp D, Peng J. Kernel VA-fles for Relevance Feedback Retrieval[C]//Proceedings of the ACM International Workshop on Multimedia Databases,2003,48~54.
[138] Cui Y, et al. Indexing the Distance: An Efficient Method to KNNProcessing [C]//Proceedings of VLDB,2001,421~430.
[139] Chakrabarti K, Mehrotra S. The Hybrid Tree：An Index Structure forHigh-dimensional Feature Space [C]//Proceedings of ICDE,1999,440~447.
[140] Ciaccia P, Patella M, Zezula P. M-tree: An Efficient Access Method forSimilarity Search in Metric Spaces [C]//Proceedings of VLDB,1997,426~435.
[141] Yianiios P. Data Structures and Algorithms for Nearest Neighbor Search inGeneral Metric Spaces [C]//Proceedings of ACM-SIAM Symposium on DiscreteAltgorithms,1993,311~321.
[142] Bozkaya T, Ozsoyoglu M, Distance-based Indexing for High-dimensionalMetric Spaces [C]//Proceedings of ACM SIGMOD,1997,26(2):357~368.
[143] Burkhard W, Keller R. Some Approaches to Best-match File Searching [J].Communications of ACM,1973,16(4):230~236.
[144] Baeza-Yates R, Cunto W, Manber U, et al. Proximity Matching UsingFixed-queries Trees [J]. Lecture Notes on Computer Science1994,807:198~212.
[145] kalantari I, McDonald G. A Data Structures and an Algorithm for theNearest Point Problem [J]. IEEE Transactions on Software Engineering,1983,9(5):631~634.
[146] Ciaccia P, Patella M. The M2-tree: Processing Complex Multi-FeatureQueries with Just One Index [C]//Proceedings of DELOS Workshop: InformationSeeking, Searching and Querying in Digital Libraries,2000.
[147] Train C, Traina A, Seeger B, et al. Slim-tree: High Performance MetricTrees Minimizing Overlap between Nodes [C]//Proceedings of EDBT,2000,51~65.
[148] Brin S. Near Neighbor Search in Large Metric Spaces [C]//Proceedings ofVLDB,1995,574~584.
[149] Navarro G. Searching in Metric Spaces by Spatial Approximation[C]//Proceedings of String Processing and Information Retrieval,1999,141~148.
[150] Tuncel E, Ferhatosmanoglu H, Rose K. Vq-Index: An Index Structure forSimilarity Searching in Multimedia Databases [C]//Proceedings of ACM MM,2002,543~552.
[151] Gionis A, Indyk P, Motwani R. Similarity Search in High Dimensions viaHashing [C]//Proceedings of VLDB,1999,518~529.
[152] Datar M, Immorlica N, Indyk P, et al. Locality-sensitive Hashing Schemebased on p-stable Distributions [C]//Proceedings of the20th ACM Symposium onComputational Geometry,2004,253~262.
[153] Athitsos V, Potamias M, Papapetrou P, et al. Nearest Neighbor RetrievalUsing Distance-based Hashing [C]//Proceedings of ICDE,2008,327~336.
[154] Bawa M, Condie T, Ganesan P. Lsh Forest: Self-tuning Indexes forSimilarity Search [C]//Proceedings of WWW,2005,651~660.
[155] Tao Y, Yi K, Sheng C, et al. Quality and Efficiency in High DimensionalNearest Neighbor Search [C]//Proceedings of ACM SIGMOD,2009,563~576.
[156] He J, Li M, Zhang H-J, et al. Generalized Manifold-Ranking-Based ImageRetrieval [J]. IEEE Transactions On Image Processing,2006,15(10):3170~3177.
[157] Berretti S, Bimbo A, Vicario E. Efficient Matching and Indexing of GraphModels in Content-Based Retrieval [J]. IEEE Transactions on Pattern Analysis AndMachine Intelligence,2011,23(10):1089~1105.
[158] Beis J S, Lowe D G. Shape Indexing Ising Approximate Nearest-neighborSearch in High-dimensional Spaces [C]//Proceeding of CVPR,1997:1000~1006.
[159] Itti L, Koch C, Niebur E. A Model of Saliency-based Visual Attention forRapid Scene Analysis [J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,1998,20:1254~1259.
[160] Koch C, Ullman S. Shifts in Selection in Visual Attention: Toward theUnderlying Neural Circuitry [J]. Human Neurobiology,1985,4:219~227.
[161] Ma Y, Zhang H. Contrast-based Image Attention Analysis by Using FuzzyGrowing [C]//Proceeding of ACM International Conference on Multimedia,2003,374~449.
[162] Bruce N, Tsotsos J. Saliency Based on Information Maximization [J].Advances in Neural Information Processing Systems,2006,18:155~162.
[163] Meur O, Callet O, Barba D, et al. A Coherent Computational Approach toModel Bottom-up Visual Attention [J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,2006,28,802~817.
[164] Liu T, Sun J, Zheng N, et al. Learning to Detect a Salient Object [J]. IEEETransaction on Pattern Analyze and Machine Intelligent,2007,33(2):353~367.
[165] Harel J, Koch C, Perona P. Graph-based Visual Saliency [J]. Advances inNeural Information Processing Systems,2007,19:545~552.
[166] Hou X, Zhang L. Saliency Detection: A Spectral Residual Approach[C]//Proceeding of IEEE Conference on Computer Vision and Pattern Recognition,2007,1~8.
[167] Achanta R, Hemami S, Estrada F, et al. Frequency-tuned Salient RegionDetection [C]//Proceeding of IEEE Conference on Computer Vision and PatternRecognition,2009,1597~1604.
[168] Salamuniccar G, Loncaric S, Open Framework for Objective Evaluation ofCrater Detection Algorithms with First Test-field Subsystem Based on Mola Data [J].Advances in Space Research,2007,42:6~19.
[169] Sawabe Y, Matsunaga T, Rokugawa S. Automated Detection andCclassification of Lunar Craters Using Multiple Approaches [J]. Advances in SpaceResearch,2006,37:21~27.
[170] Matsumoto N, Asada N, Demura H. Automatic Crater Recognition onDigital Terrain Model [C]//Proceeding of Lunar and Planetary Science XXXVI,2005,abstract no:1995.
[171] Leroy B, Medioni G, Johnson E. Crater Detection for Autonomous Landingon Asteroids [J]. Image and Vision Computing,2001,19:787~792.
[172] Kim J, Muller J, van Gasselt S, et al. Automated Crater Detection, a NewTool for Mars Cartography and Chronology [J]. Photogrammetric Engineering andRemote Sensing,2005,71,1205~1217.
[173] Ding W, Stepinski T, Bandeira L, et al. Automatic Detection of Craters inPlanetary Images: An Embedded Framework Using Feature Selection and Boosting,
[C]//Proceeding of CIKM,2009,749~758.
[174] Barata T, Alves E, Saraiva J. Automatic Recognition of Impact Craters onthe Surface of Mars [J]. Lecture Notes in Computer Science,2004,3212:489~496.
[175] Magee M, Chapman C, Dellenback S, et al. Automated Identification ofMartian Craters Using Image Processing [C]//Proceeding of Lunar and PlanetaryScience XXXIV,2003, abstract no:1756.
[176] Honda R, Iijima Y, Konishi O. Mining of Topographic Feature fromHeterogeneous Imagery and Its Application to Lunar Craters [C]//Proceeding ofProgress of Discovery Science,2002,395~407.
[177] Bandeira L, Saraiva J, Pina P. Impact Crater Recognition on Mars based ona Probability Volume Created by Template Matching [J]. IEEE Transactions onGeoscience and Remote Sensing,2007,45:4008~4015.
[178] Urbach E, Stepinski T. Automatic Detection of Sub-km Craters in HighResolution Planetary Images [J]. Planetary and Space Science,2009,57:880~887.
[179] Bue B, Stepinski T. Machine Detection of Martian Impact Craters fromDigital Topography Data [J]. IEEE Transactions on Geoscience and Remote Sensing,2007,45:265~274.
[180]岳宗玉,刘建忠,吴淦国.应用面向对象分类方法对月球撞击坑进行自动识别[J].科学通报,2008,53(22):3699~3704.
[181] Michael G, Liu J, Wu G. Coordinate Registration by Automated CraterRecognition [J]. Planetary and Space Science,2003,51:563~568.
[182] Earl J, Chicarro A, Koeberl C, et al. Automatic Recognition of Crater-likeStructures inTerrestrial and Planetary Images [C]//Proceeding of Lunar and PlanetaryScience XXXVI,2005, abstract no:1319.
[183] Burl M, Stough T, Colwell W, et al. Automated Detection of Craters andOther Geological Features [C]//Proceeding of International Symposium on ArtificialIntelligence, Robotics, and Automation in Space,2001.
[184] Cheng Y, Johnson A, Matthies L, et al. Optical Landmark Detection forSpacecraft Navigation [C]//Proceeding of Annual AAS/AIAA Space Flight MechanicsMeeting,2002,1785~1803.
[185] Martins R, Pina P, Marques J, et al. Crater Detection by a BoostingApproach [J]. IEEE Geoscience and Remote Sensing Letters,2009,6:127~131.
[186] Tian Q, Wu Y, Huang S. Combine User Defined Region-Of-Interest andSpatial Layout for Image Retrieval [C]//Proceeding of ICIP,2000,746~749.
[187] Carson C, Belongie S, Greenspan H, et al. Blobworld: Image SegmentationUsing Expectation-maximization and Its Application to Image Querying [J]. IEEETransaction on Pattern Analyze and Machine Intelligent,2002,24(8):1026~1038.
[188] Sivic J, Zisserman A. Video Google: A Text Retrieval Approach to ObjectMatching in Videos [C]//Proceeding of ICCV,2003,2:1470~1477.
[189] Hurtut T, Gousseau Y, Schmitt F. Adaptive Image Retrieval Based on theSpatial Organization of Colors [J]. Computer Vision and Image Understanding,2005,112:101~113.
[190] Lazebnik S, Schmid C, Ponce J. Beyond Bags of Features: Spatial PyramidMatching for Recognizing Natural Scene Categories [C]//Proceedings of CVPR,2006,2167~2178.
[191] Liu J, Zhang D-S, Liu G-J. Region-based Image Retrieval with High-levelSemantics Using Decision Tree Learning [J]. Pattern Recognition,2008,41:2554~2570.
[192]徐丹,章毓晋.基于空间关系的子区域最佳匹配算法[J].电子与信息学报,2005,27(8):1177~1181.
[193]韩东,峰李文,辉郭武.基于潜在局部区域空间关系学习的物体分类算法[J].计算机学报,2007,30(8):1286~1294.
[194]高科,林守勋,张勇东,唐胜.基于空间上下文的目标图像检索[J].计算机辅助设计与图形学学报,2008,20(11):1452~1458.
[195]江悦,王润生,王程.采用上下文金字塔特征的场景分类[J].计算机辅助设计与图形学学报,2010,22(8):1366~1373.
[196] Cortes C, Vapnik V. Support Vector Networks [J]. Machine Learning,1995,20:273~297.
[197] Vapnik V. The Nature of Statistical Learning Theory [M]. Springer-VerlagNew York, New York, NY, USA,1995.
[198] Kim C. Agrawala A. Analysis of the Fork-Join Queue [J]. IEEETransactions on Computers,1989,38(2):250~255.
[199] Evans D, Yousif N. Analysis of the Performance of the Parallel QuicksortMethod [J]. BIT,1985(25):106~112.
[200]崔江涛,高维索引技术中向量近似方法研究[D].博士论文.西安:西安电子科技大学.2005.
[201] Petri C A, Reisig W. Petri Net [OL]. Scholarpedia,2008,3(4):6477.
[202]袁崇义. Petri网原理与应用[M].北京:电子工业出版社,2005.
[203]李文,高鹏,陈英武,等.遥感数据处理任务调度的Petri网模型及求解算法[J].国防科技大学学报,2011,33(1):138~142.
[2] NASA. CLEMENTINE Website [OL]. http://nssdc.gsfc.nasa.gov/planetary/clementine.html.
[3] NASA. LRO Website [OL]. http://www.nasa.gov/lro/.
[4] ESA. SMART-1Website [OL]. http://www.esa.int/esaMI/SMART-1/index.html.
[5] JAXA. SELENE Website [OL]. http://www.kaguya.jaxa.jp.
[6] ISRO. Chandrayaan-1Website [OL]. http://www.isro.org/chandrayaan/.
[7]欧阳自远.我国月球探测的总体科学目标与发展战略[J].地球科学进展,2004,19(3):351~358.
[8]中科院国家天文台.嫦娥月球数据发布网站[OL]. http://moon.bao.ac.cn.
[9] Smeulders A.W.M, Worring M, Santini S, et al. Content-Based ImageRetrieval at the End of the Early Years [J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,2000,22(12):1349~1380.
[10]章毓晋.基于内容的视觉信息检索[M].北京:科学出版社,2003.
[11]周明全,耿国华,韦娜.基于内容图像检索技术[M].北京:清华大学出版社,2007.
[12]马修军.多媒体数据库与内容检索[M].北京:北京大学出版社,2007.
[13] Google [OL]. http://www.google.com.
[14] Bing [OL]. http://www.bing.com.
[15]百度识图[OL]. http://stu.baidu.com/.
[16] Montoya Zegarra J, Leite N, Silva Torres R. Wavelet-based FingerprintImage Retrieval [J]. Journal of Computational and Applied Mathematics,2009,227(2):294~307.
[17] Cappelli R, Ferrara M. A Fingerprint Retrieval System Based on Level-1andLevel-2Features [J]. Expert Systems with Applications,2012,39(12):10465~10478.
[18] Gao Y, Qi Y. Robust Visual Similarity Retrieval in Single Model FaceDatabases [J]. Pattern Recognition,2005,38(7):1009~1020.
[19] Park U, Jain A. Face Matching and Retrieval Using Soft Biometrics [J]. IEEETransactions on Information Forensics and Security,2010,5(3):406~415.
[20] Gold M, Thies C, Fischer B. A Generic Concept for the Implementation ofMedical Image RetrievalSystems [J]. International Journal of Medical Informatics,2007,76(2-3):252~259.
[21] Rahman M, Desai B, Bhattacharya P. Medical Image Retrieval withProbabilistic Multi-class Support Vector Machine Classifiers and Adaptive SimilarityFusion [J]. Computerized Medical Imaging and Graphics,2008,32(2):95~108.
[22] Kato T. Database Architecture for Content-based Image Retrieval [C]//Proceedings of SPIE on Image Storage and Retrieval Systems,1992,1662:112~123.
[23] Datta R, Joshi D, Li J, et al. Image Retrieval: Ideas, Influences, and Trends ofthe New Age [J], ACM Computing Surveys,2008,40(2):5:1~5:60.
[24] Flickner M, Sawhney H, Niblack w, et a1. Query by Image and VideoContent: the QBIC System [J]. IEEE Computer,1995,28(9):23~32.
[25] Niblack w, Zhu X, Hafner J, et al. Updates to the QBIC System [J]. SPIE,1997,3312:150~161.
[26] Gupta A, Jain R. Visual Information Retrieval [J]. Communications of ACM,1997,40(5):70~79.
[27] Bach J, Fuller C, Gupta A, et al. The Virage Image Search Engine: An OpenFamework for Image Management [C]//Proceedings of SPIE Storage and Retrieval forImage and Video Database,1996,2670:76~87.
[28] Pentland A, Picard R, Sclaroff S. Photobook: Tools for Content-basedManipulation of Image Database [C]//Proceedings of SPIE Storage and Retrieval forImage and Video Database,1994,2185:34~47.
[29] Smith J, Chang S. VisualSEEK: A Fully Automated Content-based ImageQuery System [C]//Proceedings of ACM Multimedia,1996,87~98.
[30] Smith J, Chang S. Visually Searching the Web for Content [J]. IEEEMultimedia Magazine,1997,4(3):12~20.
[31] Huang T, Mehrotra S, Ranlchandran K. Multimedia Analysis and RetrievalSystem (MARS) Project [C]//Proceedings of33th Annual Clinic on Library Applicationof Data Processing Digital Image Access and Retrieval,1996,101~117..
[32] Rui Y, Huang T, Ortega M, et al. Relevance Feedback: A Power Tool forInteractive Content-Based Image Retrieval [J]. IEEE Transactions on Circuits andSystems for Video Technology,1998,8(5):644~655.
[33] TinEye [OL]. http://tineye.com/.
[34]白雪生,廖春元,徐光佑,史元春. ImgRetr—一个基于内容的图像检索系统[C].第七届全国多媒体技术学术年会,1998.
[35] Jing F, Zhang B, Lin F, et al. A Novel Region-Based Image Retrieval MethodUsing Relevance Feedback [C]//Proceedings of the3rd International ACM Workshopon Multimedia Information Retrieval,2001,28~31.
[36]朱兴全,张宏江等. iFind:一个结合语义和视觉特征的图像相关反馈检索系统[J].计算机学报,2002,7:681~689.
[37]李鹏杰,杨树元.一种基于内容的图像检索系统lmageHunter[J].微计算机应用,2001,5:139~141.
[38]中国科学院成果鉴定报告.基于特征的多媒体信息检索系统MIRES（国家863高技术计划通信技术主题资助项目）1999,12,15.
[39]袁昕,朱淼良,王东辉.基于图像主色特征的CBIR[J].计算机辅助设计与图形学学报,2002,14(11):1061~1065.
[40]袁昕,朱淼良.基于主色匹配的图像检索系统[J].计算机辅助设计与图形学学报,2000,12,917~921.
[41]孙君顶,基于内容的图像检索技术研究[D].博士论文.西安:西安电子科技大学.2005.
[42]赵珊,基于内容的图像检索关键技术研究[D].博士论文.西安:西安电子科技大学.2007.
[43]安志勇,基于内容的图像检索关键技术研究[D].博士论文.西安:西安电子科技大学.2008.
[44]周向东,图像数据库检索中的关键技术研究[D].博士论文.上海:复旦大学.2003.
[45]张静,视频信息检索研究[D].博士论文.上海:复旦大学.2006.
[46]沈项军,基于语义学习的图像检索研究[D].博士论文.合肥:中国科学技术大学.2006.
[47]李杰,基于内容的图像检索方法研究[D].博士论文.合肥:中国科学技术大学.2008.
[48]梁栋,基于内容的图像检索中若干关键问题研究[D].博士论文.上海:上海交通大学.2005.
[49]韦娜,基于内容图像检索关键技术研究[D].博士论文.西安:西北大学.2006.
[50]夏定元,基于内容的图像检索通用技术研究及应用[D].博士论文.武汉:华中科技大学.2004.
[51] Kelly P, Cannon M, Hush D. Query by Image Example: the CANDIDApproach [C]//Proceedings of SPIE,1995,2420:238~248.
[52] Kelly P, Cannon M. CANDID: Comparison Algorithm for Navigating DigitalImage Databases [C]//Proceedings of the7th International Working Conference onScientific and Statistical Database Management,1994,252~258.
[53] Ma W, Manjunath B. Netra: A Toolbox for Navigating Large ImageDatabases [J]. Multimedia System,1999,17(3): l84~198.
[54] UCSB. Alexandria Website [OL]. http://www.alexandria.ucsb.edu/.
[55] Schroder M, Rehrauer H, Seidel K, et al. Interactive Learning andProbabilistic Retrieval in Remote Sensing Image Archives [J]. IEEE Transactions onGeoscience and Remote Sensing,2000,38(5):100~119.
[56] Seidel K, Schroder M, Rehrauer H, et al. Query by Image Content fromRemote Sensing Archives [C]//Proceedings of IGARSS,1998,1:393~396.
[57] Bretschneider T.(RS)2I-Retrieval System for Remotely Sensed ImageryOn-board Image Processing for the NTU Satellite X-Sat [EB/OL]. DOI:10356/2313.
[58] Bretschneider T, Kao O. A Retrieval System for Remotely Sensed I magery[C]//Proceedings of the International Conference on Imaging Science, Systems, andTechnology,2002,2:439~445.
[59] Carson C, Thomas M, Belongie S, et al. Blobworld: a System forRegion-Based Image Indexing and Retrieval [C]//Proceedings of InternationalConference on Visual Information Systems,1999,660~672.
[60] Datcu M, Daschiel H, Pelizzari A, et al. Information Mining in RemoteSensing Image Archives: System Concepts [J]. IEEE Transactions on Geoscience andRemote Sensing,2003,41(12):2923~2936.
[61] Shyu C-R, Klaric M, Scott G, er al. GeoIRIS: Geospatial InformationRetrieval and Indexing System—Content Mining, Semantics Modeling, and ComplexQueries [J]. IEEE Transactions on Geoscience and Remote Sensing,2007,45(4):839~852.
[62]周焰,李德仁,徐长勇.基于形状的遥感图像检索系统[J].华中科技大学学报自然科学版,2003,31(3):14~16.
[63]程起敏.基于内容的遥感影像库检索关键技术研究[D].博士论文.北京:中国科学院遥感应用研究所.2004.
[64]李锋,曾志明,付琨,吴一戎.遥感影像数据库基于内容检索系统的构建[J].武汉大学学报信息科学版,2005,30(9):787~790.
[65]曾志明.基于内容检索的遥感影像数据库系统研究[D].硕士论文.北京:中国科学院电子学研究所.2005.
[66]李嘉.基于内容的多光谱遥感影像检索若干关键技术研究[D].博士论文.武汉:华中科技大学.2009.
[67]杜培军.高光谱遥感影像检索理论与方法的研究[D].博士后出站报告.上海:上海交通大学.2004.
[68] Meyer C, Deans M. Content Based Retrieval of Images for PlanetaryExploration [C]//Proceedings of the2007IEEE/RSJ International Conference onIntelligent Robots and Systems,2007:1377~1382.
[69] NASA. Apollo Website [OL]. http://www.nasa.gov/nssdc.nasa.gov/planetary/lunar/apollo.html.
[70] NASA. Mars Global Surveyor Mars Orbiter Camera Image Gallery [OL].http://www.msss.com/moc_gallery/.
[71] NASA. Mars Exploration Rover Mission Home [OL]. http://marsrovers.jpl.nasa.gov/home/index.html.
[72] Cheng H-T, Sun F-T, Buthpitiya S. Lunar Image Classification for TerrainDetection [J]. Lecture Notes on Computer Science2010,6455:1~8.
[73] Cover T, Hart P. Nearest Neighbor Pattern Classification [J]. IEEETransactions on Information Theory,1967,13(1):21~27.
[74] Quinlan J R. Introduction of Decision Tree [J]. Machine Learning,1986,1(1):81~109.
[75] Cheng H-T, Sun F-T, Buthpitiya S, et al. Lunar Image Labeling Tool [OL].http://www.ece.cmu.edu/~hengtzec/projects/lunar/LILT.zip
[76]李珂,陈建平.嫦娥数据管理系统设计与实现[J].计算机工程,2012,38(4):218~220.
[77]李莎莎.月球科学数据编目处理与集成管理技术研究[D].硕士论文.长沙:国防科学技术大学.2010.
[78]余盼盼.月球空间数据共享发布技术研究[D].硕士论文.长沙:国防科学技术大学.2010.
[79]刘实.全局与局部特征相结合的遥感影像检索方法研究[D].硕士论文.长沙:国防科学技术大学.2010.
[80]刘潺.月球探测空间数据集成与检索技术研究[D].硕士论文.长沙:国防科学技术大学.2011.
[81] Swain M J, Ballard D H. Color Indexing [J]. International Journal ofComputer Vision,1991,7(1):11~32.
[82] Gong Y, Zhang H J, Chua T C. An Image Database System with ContentCapturing and Fast Image Indexing Abilities [C]//Proceedings of IEEE InternationalConference on Multimedia Computing and Systems,1994,121~130.
[83] Stricker M, Orengo M. Similarity of Color Images [C]//Proceedings of SPIEStorage and Retrieval for Image and Video Databases,1995,2185:381~3925.
[84] Smith J R, Chang S-F. Tools and Techniques for Color Image Retrieval[C]//Proceedings of SPIE Storage and Retrieval for Image and Video Databases,1995,2670:426~437.
[85] Pass G, Zabih R. Histogram Refinement for Content-based Image Retrieval
[C]//Proceedings of IEEE Workshop on Applications of Computer Vision,1996,96~102.
[86] Huang J, et al. Image Indexing Using Color Correlogram [C]//Proceedings ofIEEE International Conference on Computer Vision and Pattern Recognition,1997,762~768.
[87] Tamura H, Mori S, Yamawaki T. Texture Features Corresponding to VisualPerception [J]. IEEE Transactions on Systems, Man, and Cybernetics,1978,8(6):460~473.
[88] Niblack et al. The QBIC Project: Querying Images by Content Using Color,Texture, and Shape [C]//Proceedings of SPIE, Storage and Retrieval for Image andVideo Databases,1993,1908:173~187.
[89] Mao J, Jain A K. Texture Classification and Segmentation UsingMultiresolution Simultaneous Autoregressive Models [J]. Pattern Recognition,1992,25(2):173~188.
[90] Li S Z. Markov Random Field Modeling in Image Analysis [M]. Dordrecht:Springer Press,2009.
[91] Chang T, Jay Kuo C. Texture Analysis and Classification withTree-structured Wavelet Transform [J]. IEEE Transactions on Image Processing,1993,2(4):429~441.
[92] Laine A, Fan J. Texture Classification by Wavelet Packet Signatures [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(11):1186~1191.
[93] Robert M. Haralick K. Shanmugam D. Texture Features for ImageClassification [J]. IEEE Transactions on Systems, Man, and Cybernetics,1973,3(6):610~621.
[94] Daugman J G. Complete Discrete2D Gabor Transforms by Neural Networksfor Image Analysis and Compression [J]. IEEE Transactions on ASSP,1998,36:1169~1179.
[95] Bovic A, Clark M, Geisler W. Multichannel Texture Analysis UsingLocalized Spatial Filters [J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,1990,12:55~73.
[96] Gross M, Koch R, Lippert Li, et al. Multiscale Image Texture Analysis inWavelet Spaces [C]//Proceedings of IEEE International Conference on ImageProcessing,1994,3:412~416.
[97] Thyagarajan K, Nguyen T, Persons C. A Maximum Likelihood Approach toTexture Classification Using Wavelet Transform [C]//Proceedings of IEEE InternationalConference on Image Processing,1994,2:640~644.
[98] Hu M K. Visual Pattern Recognition by Moment Invariants [J]. IRETransactions on Information Theory,1962,8:179~187.
[99] Chen C C. Improved Moment Invariants for Shape Discrimination [J]. PatternRecognition,1993,26(5):683~686.
[100]庄越挺.智能多媒体信息分析与检索的研究[D].博士论文.杭州:浙江大学.1998.
[101] Zhang D, Image Retrieval Based on Shape [D]. PhD Thesis, MonashUniversity,2002.
[102] Pentland A, Picard R, Sclaroff S. Photobook: Comtent-based Manipulationof Image Databases [J]. International Journal of Computer Vision,1996,18(3):233~254.
[103] Esther M, Arkin L, Chew D, et al. An Efficienly Computable Metric forComparing Polygonal Shapes [J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,1991,13(3):209~216.
[104] Gene C, Chuang H, and Jay C. Wavelet descriptor of Planar Curves: Theoryand Applications [J]. IEEE Transactions on Image Processing,1996,5(1):56~70.
[105] Huang W, Gao Y, Chan K. A Review of Region-Based Image Retrieval [J].Journal of Signal Processing System,2010,59:143~161.
[106] Almeida J, Rocha A, Torres R, et al. Making Colors Worth More Than aThousand Words [C]//Proceedings of the ACM Symposium on Applied Computing,2008,1180~1186.
[107] Sun Y, Ozawa S. A Hierarchical Approach for Region-based ImageRetrieval.[C]//Proceedings of the IEEE Conference on Computer Vision and PatternRecognition,2004,2:996~1001.
[108] Chen Y, Wang J. A Region-based Fuzzy Feature Matching Approach toContent-based Image Retrieval [J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,2002,24:1252~1267.
[109] Xiong W, Qiu B, Tian Q, et al. MultiPRE: A Novel Framework withMultiple Parallel Retrieval Engines for Content-based Image Retrieval [C]//Proceedingsof the ACM International Ionference on Multimedia,2005,1023~1032.
[110] Harris C, Stephens M.1988. A Combined Corner and Edge Detector.[C]//Proceedings of Alvey Vision Conference,1988,147~151.
[111] Smith S M, Brady J M. SUSAN-A New Approach to Low Level ImageProcessing [J]. Journal of Computer Vision,1997,23(1):45~78.
[112] Mikolajczyk K, Schmid C. Scale&Affine Invariant Interest Point Detectors[J]. International Journal of Computer Vision,2004,60(1):63~86.
[113] Mikolajczyk K, Schmid C. Indexing Based on Scale Invariant InterestPoints [C]//Proceedings of ICCV,2001,525~531.
[114] Lowe D. Distinctive Image Features from Scale Invariant Keypoints [J].International Journal of Computer Vision,2004,60(2):92~110.
[115] Ke Y, Sukthankar R. PCA-SIFT:“A More Distinctive Representation forLocal Image Descriptors”[C]//Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition,2004,511~517.
[116] Mikolajczyk K, Tuytelaars T, Schmid C, et al. A Comparison of AffineRegion Detectors [J]. International Journal of Computer Vision,2005,65:43~72.
[117] Bay S H, Tuytelaars T, Gool L V. Surf: Speeded Up Robust Features [C]//Proceedings of European Conference on Computer Vision,2006:404~417.
[118] HE J, LI M, ZHANG H-J, et al. Manifold-ranking Based Image Retrieval[C]//Proceedings of the ACM International Conference on Multimedia,2004,9~16.
[119] Vasconcelos N, Lippman A. A Multiresolution Manifold Distance forInvariant Image Similarity [J]. IEEE Transactions on Multimedia,2005,7(1):127~142.
[120] Kullback S, Burnham K P, Laubscher N F. The Kullback-Leibler Distance[J]. The American Statistician,1987,41(4):340~344.
[121] Do M N. Vetterli M. Wavelet-Based Texture Retrieval Using GeneralizedGaussian Density and Kullback-Leibler Distance [J]. IEEE Transactions on ImageProcessing.2002,11(2):146~158.
[122] Jing F, Li M-J, Zhang H-J, et al. An Efficient and Effective Region-basedImage Retrieval Framework [J]. IEEE Transactions on Image Processing,2004,13(5):699~709.
[123] Wang J, Li J, Wiederhold G. Simplicity: Semantics-Sensitive IntegratedMatching for Picture Libraries [J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,2001,23(9):947~963.
[124] Rubner Y, Tomasi C, Guibas L. The Earth Mover’s Distance as a Metric forImage Retrieval [J]. International Journal of Computer Vision,2000,40(2),99~121.
[125]董道国,高维数据索引结构研究[D].博士论文.上海:复旦大学.2005.
[126] Guttman A. R-tree: A Dynamic Index Structure for Spatial Searching [C]//Proceedings of SIGMOD,1984,47~57.
[127] Sellis T, Roussopoulos N, Faloutsos C. The R+-Tree: A Dynamic Index forMulti-Dimensional Objects [C]//Proceedings of VLDB,1987,507~518.
[128] Beckmann N, Kriegel H-P, Schneider R, et al. The R*-tree: An Efficient andRobust Access Method for Points and Rectangles [C]//Proceedings of SIGMOD,1990,322~331.
[129] Berchtold S, Keim D, Kriegel H-P. The X-tree: An Index Structure forHigh-dimensional Data [C]//Proceedings of VLDB,1996,28~39.
[130] White D, Jain R. Similarity Indexing with the SS-tree [C]//Proceedings ofICDE,1996,516~523.
[131] Katayama N, Satoh S. The SR-tree: An Index Structure forHigh-dimensional Nearest Neighbor Queries [C]//Proceedings of SIGMOD,1997,369~380.
[132] Weber R, Schek H, Blott S. A Quantitative Analysis and Performance Studyfor Similarity-Search Methods in High-Dimensional Spaces [C]//Proceedings of VLDB,1998,194~205.
[133] Ferhatosmanoglu H, Tuncel E, Agrawal D, et al. Approximate NearestNeighbor Searching in Multimedia Databases [C]//Proceedings of ICDE,2001,503~511.
[134] Berchtold S, Bohm C, Jagadish H, et al. Independent quantization: An IndexCompression Technique for High-dimensional Data Spaces [C]//Proceedings of ICDE,2000,577~588.
[135] Cha G, Zhu X, Petkovic D, et al. An Efficient Indexing Method for NearestNeighbor Searches in High-Dimensional Image Databases [J]. IEEE Transactions onMultimedia,2002,4(1):76~87.
[136] Sakurai Y, Yoshikawa M, Uemura S, et al. The A-tree: An Index Structurefor High-dimensional Spaces Using Relative Approximation [C]//Proceedings of VLDB,2000,516~526.
[137] Heisterkamp D, Peng J. Kernel VA-fles for Relevance Feedback Retrieval[C]//Proceedings of the ACM International Workshop on Multimedia Databases,2003,48~54.
[138] Cui Y, et al. Indexing the Distance: An Efficient Method to KNNProcessing [C]//Proceedings of VLDB,2001,421~430.
[139] Chakrabarti K, Mehrotra S. The Hybrid Tree：An Index Structure forHigh-dimensional Feature Space [C]//Proceedings of ICDE,1999,440~447.
[140] Ciaccia P, Patella M, Zezula P. M-tree: An Efficient Access Method forSimilarity Search in Metric Spaces [C]//Proceedings of VLDB,1997,426~435.
[141] Yianiios P. Data Structures and Algorithms for Nearest Neighbor Search inGeneral Metric Spaces [C]//Proceedings of ACM-SIAM Symposium on DiscreteAltgorithms,1993,311~321.
[142] Bozkaya T, Ozsoyoglu M, Distance-based Indexing for High-dimensionalMetric Spaces [C]//Proceedings of ACM SIGMOD,1997,26(2):357~368.
[143] Burkhard W, Keller R. Some Approaches to Best-match File Searching [J].Communications of ACM,1973,16(4):230~236.
[144] Baeza-Yates R, Cunto W, Manber U, et al. Proximity Matching UsingFixed-queries Trees [J]. Lecture Notes on Computer Science1994,807:198~212.
[145] kalantari I, McDonald G. A Data Structures and an Algorithm for theNearest Point Problem [J]. IEEE Transactions on Software Engineering,1983,9(5):631~634.
[146] Ciaccia P, Patella M. The M2-tree: Processing Complex Multi-FeatureQueries with Just One Index [C]//Proceedings of DELOS Workshop: InformationSeeking, Searching and Querying in Digital Libraries,2000.
[147] Train C, Traina A, Seeger B, et al. Slim-tree: High Performance MetricTrees Minimizing Overlap between Nodes [C]//Proceedings of EDBT,2000,51~65.
[148] Brin S. Near Neighbor Search in Large Metric Spaces [C]//Proceedings ofVLDB,1995,574~584.
[149] Navarro G. Searching in Metric Spaces by Spatial Approximation[C]//Proceedings of String Processing and Information Retrieval,1999,141~148.
[150] Tuncel E, Ferhatosmanoglu H, Rose K. Vq-Index: An Index Structure forSimilarity Searching in Multimedia Databases [C]//Proceedings of ACM MM,2002,543~552.
[151] Gionis A, Indyk P, Motwani R. Similarity Search in High Dimensions viaHashing [C]//Proceedings of VLDB,1999,518~529.
[152] Datar M, Immorlica N, Indyk P, et al. Locality-sensitive Hashing Schemebased on p-stable Distributions [C]//Proceedings of the20th ACM Symposium onComputational Geometry,2004,253~262.
[153] Athitsos V, Potamias M, Papapetrou P, et al. Nearest Neighbor RetrievalUsing Distance-based Hashing [C]//Proceedings of ICDE,2008,327~336.
[154] Bawa M, Condie T, Ganesan P. Lsh Forest: Self-tuning Indexes forSimilarity Search [C]//Proceedings of WWW,2005,651~660.
[155] Tao Y, Yi K, Sheng C, et al. Quality and Efficiency in High DimensionalNearest Neighbor Search [C]//Proceedings of ACM SIGMOD,2009,563~576.
[156] He J, Li M, Zhang H-J, et al. Generalized Manifold-Ranking-Based ImageRetrieval [J]. IEEE Transactions On Image Processing,2006,15(10):3170~3177.
[157] Berretti S, Bimbo A, Vicario E. Efficient Matching and Indexing of GraphModels in Content-Based Retrieval [J]. IEEE Transactions on Pattern Analysis AndMachine Intelligence,2011,23(10):1089~1105.
[158] Beis J S, Lowe D G. Shape Indexing Ising Approximate Nearest-neighborSearch in High-dimensional Spaces [C]//Proceeding of CVPR,1997:1000~1006.
[159] Itti L, Koch C, Niebur E. A Model of Saliency-based Visual Attention forRapid Scene Analysis [J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,1998,20:1254~1259.
[160] Koch C, Ullman S. Shifts in Selection in Visual Attention: Toward theUnderlying Neural Circuitry [J]. Human Neurobiology,1985,4:219~227.
[161] Ma Y, Zhang H. Contrast-based Image Attention Analysis by Using FuzzyGrowing [C]//Proceeding of ACM International Conference on Multimedia,2003,374~449.
[162] Bruce N, Tsotsos J. Saliency Based on Information Maximization [J].Advances in Neural Information Processing Systems,2006,18:155~162.
[163] Meur O, Callet O, Barba D, et al. A Coherent Computational Approach toModel Bottom-up Visual Attention [J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,2006,28,802~817.
[164] Liu T, Sun J, Zheng N, et al. Learning to Detect a Salient Object [J]. IEEETransaction on Pattern Analyze and Machine Intelligent,2007,33(2):353~367.
[165] Harel J, Koch C, Perona P. Graph-based Visual Saliency [J]. Advances inNeural Information Processing Systems,2007,19:545~552.
[166] Hou X, Zhang L. Saliency Detection: A Spectral Residual Approach[C]//Proceeding of IEEE Conference on Computer Vision and Pattern Recognition,2007,1~8.
[167] Achanta R, Hemami S, Estrada F, et al. Frequency-tuned Salient RegionDetection [C]//Proceeding of IEEE Conference on Computer Vision and PatternRecognition,2009,1597~1604.
[168] Salamuniccar G, Loncaric S, Open Framework for Objective Evaluation ofCrater Detection Algorithms with First Test-field Subsystem Based on Mola Data [J].Advances in Space Research,2007,42:6~19.
[169] Sawabe Y, Matsunaga T, Rokugawa S. Automated Detection andCclassification of Lunar Craters Using Multiple Approaches [J]. Advances in SpaceResearch,2006,37:21~27.
[170] Matsumoto N, Asada N, Demura H. Automatic Crater Recognition onDigital Terrain Model [C]//Proceeding of Lunar and Planetary Science XXXVI,2005,abstract no:1995.
[171] Leroy B, Medioni G, Johnson E. Crater Detection for Autonomous Landingon Asteroids [J]. Image and Vision Computing,2001,19:787~792.
[172] Kim J, Muller J, van Gasselt S, et al. Automated Crater Detection, a NewTool for Mars Cartography and Chronology [J]. Photogrammetric Engineering andRemote Sensing,2005,71,1205~1217.
[173] Ding W, Stepinski T, Bandeira L, et al. Automatic Detection of Craters inPlanetary Images: An Embedded Framework Using Feature Selection and Boosting,
[C]//Proceeding of CIKM,2009,749~758.
[174] Barata T, Alves E, Saraiva J. Automatic Recognition of Impact Craters onthe Surface of Mars [J]. Lecture Notes in Computer Science,2004,3212:489~496.
[175] Magee M, Chapman C, Dellenback S, et al. Automated Identification ofMartian Craters Using Image Processing [C]//Proceeding of Lunar and PlanetaryScience XXXIV,2003, abstract no:1756.
[176] Honda R, Iijima Y, Konishi O. Mining of Topographic Feature fromHeterogeneous Imagery and Its Application to Lunar Craters [C]//Proceeding ofProgress of Discovery Science,2002,395~407.
[177] Bandeira L, Saraiva J, Pina P. Impact Crater Recognition on Mars based ona Probability Volume Created by Template Matching [J]. IEEE Transactions onGeoscience and Remote Sensing,2007,45:4008~4015.
[178] Urbach E, Stepinski T. Automatic Detection of Sub-km Craters in HighResolution Planetary Images [J]. Planetary and Space Science,2009,57:880~887.
[179] Bue B, Stepinski T. Machine Detection of Martian Impact Craters fromDigital Topography Data [J]. IEEE Transactions on Geoscience and Remote Sensing,2007,45:265~274.
[180]岳宗玉,刘建忠,吴淦国.应用面向对象分类方法对月球撞击坑进行自动识别[J].科学通报,2008,53(22):3699~3704.
[181] Michael G, Liu J, Wu G. Coordinate Registration by Automated CraterRecognition [J]. Planetary and Space Science,2003,51:563~568.
[182] Earl J, Chicarro A, Koeberl C, et al. Automatic Recognition of Crater-likeStructures inTerrestrial and Planetary Images [C]//Proceeding of Lunar and PlanetaryScience XXXVI,2005, abstract no:1319.
[183] Burl M, Stough T, Colwell W, et al. Automated Detection of Craters andOther Geological Features [C]//Proceeding of International Symposium on ArtificialIntelligence, Robotics, and Automation in Space,2001.
[184] Cheng Y, Johnson A, Matthies L, et al. Optical Landmark Detection forSpacecraft Navigation [C]//Proceeding of Annual AAS/AIAA Space Flight MechanicsMeeting,2002,1785~1803.
[185] Martins R, Pina P, Marques J, et al. Crater Detection by a BoostingApproach [J]. IEEE Geoscience and Remote Sensing Letters,2009,6:127~131.
[186] Tian Q, Wu Y, Huang S. Combine User Defined Region-Of-Interest andSpatial Layout for Image Retrieval [C]//Proceeding of ICIP,2000,746~749.
[187] Carson C, Belongie S, Greenspan H, et al. Blobworld: Image SegmentationUsing Expectation-maximization and Its Application to Image Querying [J]. IEEETransaction on Pattern Analyze and Machine Intelligent,2002,24(8):1026~1038.
[188] Sivic J, Zisserman A. Video Google: A Text Retrieval Approach to ObjectMatching in Videos [C]//Proceeding of ICCV,2003,2:1470~1477.
[189] Hurtut T, Gousseau Y, Schmitt F. Adaptive Image Retrieval Based on theSpatial Organization of Colors [J]. Computer Vision and Image Understanding,2005,112:101~113.
[190] Lazebnik S, Schmid C, Ponce J. Beyond Bags of Features: Spatial PyramidMatching for Recognizing Natural Scene Categories [C]//Proceedings of CVPR,2006,2167~2178.
[191] Liu J, Zhang D-S, Liu G-J. Region-based Image Retrieval with High-levelSemantics Using Decision Tree Learning [J]. Pattern Recognition,2008,41:2554~2570.
[192]徐丹,章毓晋.基于空间关系的子区域最佳匹配算法[J].电子与信息学报,2005,27(8):1177~1181.
[193]韩东,峰李文,辉郭武.基于潜在局部区域空间关系学习的物体分类算法[J].计算机学报,2007,30(8):1286~1294.
[194]高科,林守勋,张勇东,唐胜.基于空间上下文的目标图像检索[J].计算机辅助设计与图形学学报,2008,20(11):1452~1458.
[195]江悦,王润生,王程.采用上下文金字塔特征的场景分类[J].计算机辅助设计与图形学学报,2010,22(8):1366~1373.
[196] Cortes C, Vapnik V. Support Vector Networks [J]. Machine Learning,1995,20:273~297.
[197] Vapnik V. The Nature of Statistical Learning Theory [M]. Springer-VerlagNew York, New York, NY, USA,1995.
[198] Kim C. Agrawala A. Analysis of the Fork-Join Queue [J]. IEEETransactions on Computers,1989,38(2):250~255.
[199] Evans D, Yousif N. Analysis of the Performance of the Parallel QuicksortMethod [J]. BIT,1985(25):106~112.
[200]崔江涛,高维索引技术中向量近似方法研究[D].博士论文.西安:西安电子科技大学.2005.
[201] Petri C A, Reisig W. Petri Net [OL]. Scholarpedia,2008,3(4):6477.
[202]袁崇义. Petri网原理与应用[M].北京:电子工业出版社,2005.
[203]李文,高鹏,陈英武,等.遥感数据处理任务调度的Petri网模型及求解算法[J].国防科技大学学报,2011,33(1):138~142.
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