基于内容的图像检索若干技术研究
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摘要
随着多媒体技术和Internet网络技术的快速发展，人们越来越多地接触到各种各样的图像信息。面对海量的图像资源，用来快速而准确地找到所需图像的检索技术成为多媒体领域的一个研究热点。近年来，基于内容的图像检索技术已成为国内外广泛关注的焦点，并在许多领域得到了广泛的应用。但是到目前为止这项技术还存在许多亟待解决的问题，本文在这方面做了一系列比较深入的研究，取得的主要成果：
第一，为了去除噪声对图像检索的影响，提出了改进的双边滤波算法和基于改进的非局部均值与非下采样Contourlet变换(NSCT)Wiener滤波的图像去噪两种算法。前者用维纳函数对当前像素点和邻域像素点灰度值进行估计，在此基础上计算像素亮度相似度权值，从而减少噪声对加权系数的干扰。重点介绍后者，后者是一种结合空域和变换域的去噪方法，首先在空域中对加噪图像运用小窗口的改进非局部均值算法去除高频噪声，然后在NSTC域中采用维纳滤波算法消除低频噪声并重建图像。同时针对非局部均值算法的去噪性能、计算复杂度、参数估计提出了改进方法。实验结果证明无论是客观评价指标上还是主观视觉效果上，本文提出的算法都有一定的优势。
第二，提出一种基于颜色-空间分布特征的图像检索算法，可以有效克服传统的颜色直方图无法反映颜色空间分布信息的缺陷。该算法既考虑了像素的位置重要性又考虑了同种颜色像素空间分布特性。以人眼视觉注意机制为基础，利用像素与其多尺度邻域颜色对比度对相应像素点进行加权处理，构造出能有效反映原始图像中像素位置重要性的加权颜色直方图。同时采用颜色空间分布离散度来描述图像中同种颜色的像素空间分布特征。仿真实验结果表明，检索结果能较好地满足人的视觉感受。
第三，准确地提取出图像中用户感兴趣的部分是解决基于区域的图像检索问题的关键。提出一种基于视觉注意力模型的图像检索算法，该算法首先采用结合高斯多尺度变换和颜色复杂度计算的视觉注意力模型生成显著图，在此基础上采用最大类间方差动态阈值法提取显著目标，并利用生成的显著图与基于Canny算子提取的初始图像边缘共同提取显著区域的边缘。最后，结合显著目标的颜色直方图和显著边缘的梯度方向直方图实现区域级的图像检索。
第四，提出一种基于视觉有效区域的颜色和对象二级语义的图像检索算法，可以有效克服图像低层视觉特征和高层语义之间的“鸿沟”。结合像素的全局显著度和角点分布信息获得包括单个感兴趣目标的视觉有效区域，在此基础上，提取有效区域的主颜色做为颜色特征语义，采用支持向量机(SVM)机器学习算法获取区域的对象语义，最后根据颜色和对象语义实现图像二级语义检索。
With the rapid development of multimedia and internet technology, people can get more and moreinformation of all kinds of images. Face to the massive image information, the content-based imageretrieval (CBIR) system which is used to rapidly and effectively search the desired images fromlarge-scale image has become a research hot in multimedia field. In recent years, CBIR is a very hotresearch direction at home and abroad and has been applied to many fields. Up to now，still manyproblems need be solved in this research field. This thesis focuses on CBIR technology and gives somecontributions as follows:
Firstly，in order to get rid of the noise impact for image retrieval, two denoising algorithmsincluding a modified bilateral filtering algorithm and an algorithm combining the improved non-localmeans and non-subsampled contourlet transform Wiener filtering are proposed. The former uses wienerfunction to estimate the values of the current and neighbor pixels, based on which the radiometricsimilarity weight values are computed. Thus, noise interference on the weighted coefficient can bereduced. The latter is a denoising algorithm integrating spatial and transform domain. In spatial domainthe highfrequency noises are firstly removed using the improved non-local means method with smallradius of the neighborhood. Then in contourlet domain the de-noised image using non-local meansmethod is re-denoised by Wiener filtering and most of low-frequency noises are removed. In non-localmeans method, three aspects about denoising performance，computational complexity and parameterestimation are improved. The experimental results show that the proposed algorithm can get denoisedimage with higher subjective visual quality and objective evaluation index.
Secondly, an image retrieval method based on color-spatial distributing feature is proposed，whichcan effectively overcome the problem that the traditional image retrieval method is prone to lose thespatial information of colors. This mehod not only utilizes the pixel position but also the same colorpixel spatial distribution characteristics. According to visual attention computational mode，theweighted histogram which reflects the pixel position importance is constructed after all pixels areweighed by the pixel color contrast with respect to their multi-scale neighborhoods. In the meantime, the spatial relationship feature of the same or similar colors is considered by colors distributingcohesion. The experiments show that the method mentioned above has high accuracy and its retrievalresults match human visual percept well．
Thirdly, in the region-based image retrieval, key problem is to accurately extract region of interest.An image retrieval method based on visual attention model is proposed. Employing visual attentionmodel combining Gaussian multi-scale transform with color complexity measure to create salient map,based on which the salient object is extracted by max-variance method. Simultaneity edges in salientregion are extracted according to the created salient map and the initial edge map by canny detector.Color histogram of salient object and gradient direction histogram of salient edges are fused for therealization of regional image retrieval.
Fourthly, an image retrieval scheme is proposed based on color and object semantic concept ofeffective visual area, which can effectively overcome the “semantic gap” between the low levelfeature and high level semantic concept. A visual window including single object is created accordingto global sailent value of pixel and edge information based on distribution of corner points. Then，thehigh level color semantic can be obtained by using the main color of the quantized color image．Andthe high level object semantic is determined by mapping the low level feature using SVM Machinelearning. Finally，image retrieval is implemented by using these two-level high semantic concepts.
第一，为了去除噪声对图像检索的影响，提出了改进的双边滤波算法和基于改进的非局部均值与非下采样Contourlet变换(NSCT)Wiener滤波的图像去噪两种算法。前者用维纳函数对当前像素点和邻域像素点灰度值进行估计，在此基础上计算像素亮度相似度权值，从而减少噪声对加权系数的干扰。重点介绍后者，后者是一种结合空域和变换域的去噪方法，首先在空域中对加噪图像运用小窗口的改进非局部均值算法去除高频噪声，然后在NSTC域中采用维纳滤波算法消除低频噪声并重建图像。同时针对非局部均值算法的去噪性能、计算复杂度、参数估计提出了改进方法。实验结果证明无论是客观评价指标上还是主观视觉效果上，本文提出的算法都有一定的优势。
第二，提出一种基于颜色-空间分布特征的图像检索算法，可以有效克服传统的颜色直方图无法反映颜色空间分布信息的缺陷。该算法既考虑了像素的位置重要性又考虑了同种颜色像素空间分布特性。以人眼视觉注意机制为基础，利用像素与其多尺度邻域颜色对比度对相应像素点进行加权处理，构造出能有效反映原始图像中像素位置重要性的加权颜色直方图。同时采用颜色空间分布离散度来描述图像中同种颜色的像素空间分布特征。仿真实验结果表明，检索结果能较好地满足人的视觉感受。
第三，准确地提取出图像中用户感兴趣的部分是解决基于区域的图像检索问题的关键。提出一种基于视觉注意力模型的图像检索算法，该算法首先采用结合高斯多尺度变换和颜色复杂度计算的视觉注意力模型生成显著图，在此基础上采用最大类间方差动态阈值法提取显著目标，并利用生成的显著图与基于Canny算子提取的初始图像边缘共同提取显著区域的边缘。最后，结合显著目标的颜色直方图和显著边缘的梯度方向直方图实现区域级的图像检索。
第四，提出一种基于视觉有效区域的颜色和对象二级语义的图像检索算法，可以有效克服图像低层视觉特征和高层语义之间的“鸿沟”。结合像素的全局显著度和角点分布信息获得包括单个感兴趣目标的视觉有效区域，在此基础上，提取有效区域的主颜色做为颜色特征语义，采用支持向量机(SVM)机器学习算法获取区域的对象语义，最后根据颜色和对象语义实现图像二级语义检索。
With the rapid development of multimedia and internet technology, people can get more and moreinformation of all kinds of images. Face to the massive image information, the content-based imageretrieval (CBIR) system which is used to rapidly and effectively search the desired images fromlarge-scale image has become a research hot in multimedia field. In recent years, CBIR is a very hotresearch direction at home and abroad and has been applied to many fields. Up to now，still manyproblems need be solved in this research field. This thesis focuses on CBIR technology and gives somecontributions as follows:
Firstly，in order to get rid of the noise impact for image retrieval, two denoising algorithmsincluding a modified bilateral filtering algorithm and an algorithm combining the improved non-localmeans and non-subsampled contourlet transform Wiener filtering are proposed. The former uses wienerfunction to estimate the values of the current and neighbor pixels, based on which the radiometricsimilarity weight values are computed. Thus, noise interference on the weighted coefficient can bereduced. The latter is a denoising algorithm integrating spatial and transform domain. In spatial domainthe highfrequency noises are firstly removed using the improved non-local means method with smallradius of the neighborhood. Then in contourlet domain the de-noised image using non-local meansmethod is re-denoised by Wiener filtering and most of low-frequency noises are removed. In non-localmeans method, three aspects about denoising performance，computational complexity and parameterestimation are improved. The experimental results show that the proposed algorithm can get denoisedimage with higher subjective visual quality and objective evaluation index.
Secondly, an image retrieval method based on color-spatial distributing feature is proposed，whichcan effectively overcome the problem that the traditional image retrieval method is prone to lose thespatial information of colors. This mehod not only utilizes the pixel position but also the same colorpixel spatial distribution characteristics. According to visual attention computational mode，theweighted histogram which reflects the pixel position importance is constructed after all pixels areweighed by the pixel color contrast with respect to their multi-scale neighborhoods. In the meantime, the spatial relationship feature of the same or similar colors is considered by colors distributingcohesion. The experiments show that the method mentioned above has high accuracy and its retrievalresults match human visual percept well．
Thirdly, in the region-based image retrieval, key problem is to accurately extract region of interest.An image retrieval method based on visual attention model is proposed. Employing visual attentionmodel combining Gaussian multi-scale transform with color complexity measure to create salient map,based on which the salient object is extracted by max-variance method. Simultaneity edges in salientregion are extracted according to the created salient map and the initial edge map by canny detector.Color histogram of salient object and gradient direction histogram of salient edges are fused for therealization of regional image retrieval.
Fourthly, an image retrieval scheme is proposed based on color and object semantic concept ofeffective visual area, which can effectively overcome the “semantic gap” between the low levelfeature and high level semantic concept. A visual window including single object is created accordingto global sailent value of pixel and edge information based on distribution of corner points. Then，thehigh level color semantic can be obtained by using the main color of the quantized color image．Andthe high level object semantic is determined by mapping the low level feature using SVM Machinelearning. Finally，image retrieval is implemented by using these two-level high semantic concepts.
引文
[1]姚敏.数字图像处理[M].北京:机械工业出版社,2006
[2]庄越挺，潘云鹤，吴飞.网上多媒体信息分析与检索[M].北京:清华大学出版社，2002
[3] Datta R, Joshi D, Li J, Wang J Z. Image retrieval: ideas, influences, and trends of thenew age[J]. ACM Computing Surveys,2008,40(2):1-60
[4]章毓晋.基于内容的视觉信息检索[M].北京:科学出版社，2003
[5]安志勇.基于内容的图像检索关键技术研究[D].西安电子科技大学，博士学位论文，2008
[6]冯松鹤.面向感知的图像检索及自动化标注算法研究[D].北京交通大学，博士学位论文，2009
[7]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
[8] Yue J，Li Z B, Liu L,et a1.Content-based image retrieval using color and texture fusedfeatures[J].Mathematical and Computer Modelling,2011,54:1121-1127
[9] Yang N C, Chang W H. A fast MPEG-7dominant color extraction with new similaritymeasure for image retrieval[J]. Journal of Visual Communication&ImageRepresentation.2008,19(2):92-105
[10] Wang X Y, Yu Y J, Yang H Y. An effective image retrieval scheme using color, textureand shape features [J]. Computer Standards&Interfaces,2011,33:59-68
[11]黄元元，刘宁钟.一种新的基于颜色特征的图像检索方法[J].小型微型计算机系统，2012，33(3):609-613
[12]施智平,胡宏，李清勇等,基于纹理描述子的图像检索[J],软件学报,2005,16(6):1039-1045
[13] Young Deok Chun, Nam Chul Kim, Ick Hoon Jang. Content-based image retrieval usingmultiresolution color and texture features[J], IEEE Transactions on Multimedia,2008,10(6):1073-1084
[14] Wang H, Dai F, Zhang L, Lu S X．An image retrieval method based on texture featuresof object region[C]． International Conference on Electronics and Optoelectronics(ICEOE2011),4:83-86
[15] Zeng Zhiyong，Liu Shigang．A Novel Region-based Image Retrieval Algorithm UsingHybrid Feature[C].WRI World Congresson Computer Science and InformationEngineering．Los Angeles：IEEE Computer Society，2009：416-420
[16] Chiaug Cheng Chieh，Hung Yi Ping，Yang Hsuan，et a1．Region-based Image RetrievalUsing Color-size Features of Watershed Regions[J]．Journal of Visual Communicationand Image Representation，2009，20(3)：167-177
[17]王兵，张欣，王苗等.图像主题区域提取及其在图像检索中的应用[J].小型微型计算机系统，2011,32(3):567-571
[18] Hong, Fu, Zheru，Chi. An efficient algorithm for attention-driven image interpretationfrom segments [J].Pattern Recognition,2009:126-129
[19]斯白露，高文，卢汉清等．基于感兴趣区域的图像检索方法[J]．高技术通信，2003，13(5):13-18
[20]冯松鹤，郎丛妍，须德.一种融合图学习与区域显著性分析的图像检索算法[J].电子学报，2011，39(10)：2288-2294
[21]张菁，沈兰荪，David Dagan FENG．基于视觉感知的图像检索的研究[J].电子学报，2008，36(3):494-499
[22] J Zhang，L Zhuo，L S Shen. Regions of interest extraction based on visual attentionmodel and watershed segmentation [C]. IEEE International Conf. on Neural Networks&Signal Processing, China,2008,375-378
[23]李志欣，施智平，李志清，史忠植.图像检索中语义映射方法综述[J].计算机辅助设计与图形学学报，2008，20(8):1085-1094
[24] S.C.H. Hoi, R. Jin, J. Zhu, M.R. Lyu.Semisupervised SVM batch mode active learningwith applications to image retrieval[J]. ACM Transactions on InformationSystems,2009,27(3):1-29
[25] Carneiro G，Chan A B，Moreno P J，et a1．Supervised learning of semantic classes forimage annotation and retrieval[J]．IEEE Transactions on Pattern Analysis and MachineIntelligence，2007，29(3)：394-410
[26] Mohammed Belkhatir. A three-level architecture for bridging the image semantic gap[J].Multimedia Systems,2011,17:135-148
[27] Rasiwasia N，Moreno P J，Vasconcelos N．Bridging the gap：query by semantic example[J].IEEE Transactions on Multimedia，2007,9(5):923-938
[28] Gao Y I，Fan J P，Xue X Y，et a1． Automatic image annotation by incorporatingfeature hierarchy and boosting to scale up SVM classifiers[C].Proceedings of the14thACM International Conference on Multimedia.Santa Barbara.2006：901-9l0
[29] Zhao Y F， Zhao Y, Zhu Z F. Pseudo relevance feedback for search-based imageannotation [J]. Chinese Journal of Electronics.2008,17(3):471-474
[30] R.J. Liu, Y.H. Wang, SVM-based active feedback in image retrieval using clustering andunlabeled data[J]. Pattern Recognition,2008,41(8):2645-2655
[31] M.M. Rahman, B.C. Desai, A framework for medical image retrieval using machinelearning and statistical similarity matching techniques with relevance feedback[J], IEEETransactions on Information Technology in Biomedicine,2007,11(1):58-69
[32] Jun YANG, Shijiao ZHU, Daming LIU. An Image Annotation Method Using RelevanceFeedback Log and Semantic Network[J]. Journal of Computational Information Systems,2009,5(3):1331-1337
[33] Jing F，Li M J，Zhang H J，et a1．Aunified framework for image retrieval using keywordand visual features[J]．IEEE Transactions on Image Processing,2005,14(7):979-989
[34] Wang X Y, Chen J W, Yang H Y.A new integrated SVM classifiers for relevancefeedback content-based image retrieval using EM parameter estimation[J]. Applied SoftComputing,2011,11:2787-2804
[35] Shen，H.T.，B.C.Ooi，etal.，Giving meanings to www Images[C]. Proceedings of the8thACM International Conference on Multimedia (ACM MM),2000. Los Angeles,California:39-47
[36] Srihari，R.K.，Z.Zhang，etal.，Intelligent indexing and semantic retrieval of multimodaldocuments[J]. Information Retrieval,2000.2(2):245-275
[37]张华，张森，孟祥增.基于外部信息源的WWW图像语义提取研究[J].计算机科学，2006.33(4):211-214
[38]许相莉,张利彪,刘向东,于哲舟,周春光.基于粒子群的图像检索相关反馈算法[J].小型微型计算机系统.2010,38(8):1935-1940
[39] Deselaers T, Paredes R, Vidal E, Hermann N. Learning weighted distances for relevancefeedback in image retrieval[C]. In: Proceedings of19th International Conference onPattern Recognition, Tampa, FL, United stats,2008.1-4.
[40] Miguel Arevalillo-Herráez, Francesc J. Ferri, Salvador Moreno-Picot. Distance-basedrelevance feedback using a hybrid interactive genetic algorithm for image retrieval[J].Applied Soft Computing.2011,11:1782-1791
[41] Wu J, Fu Y, Lu M. Bayesian active learning in retrieval feedback for image retrieval[C].Proceedings of2nd international symposium on intelligent information technologyapplication.China:Inst. Of Elec,2008.371-375
[42] Ves E，Domingo J，et a1．Anovel Bayesian framework for relevance feedback in imagecontent-based retrieval system [J].Pattern Recognition,2006,39:1622-1632
[43]赵玉凤,赵耀,朱振峰.贝叶斯框架下基于区域的相关反馈算法[J].电子与信息学报,2008,30(4):937-940
[44] WANG B, ZHANG X,LI N. Relevance feedback technique for content-based imageretrieval using neural network learning[C].Proceedings of the5th internationalconference on Machine Learning and Cybernetics,2006,3692-3696
[45] Grigorova A, De Natale F G B, Dagli C, Huang T S. Content-based image retrieval byfeature adaption and relevance feedback [J]. IEEE transactions on multimedia,2007,9(6):1183-1192
[46]邬俊，鲁明羽，刘闯.基于混合学习框架的SVM反馈算法研究[J].电子学报.2010,38(9):2101-2106
[47] Zhou Z-H．When semi-supervised learning meets ensemble learning [C]．Proc IntWorkshop Multiple Classifier System．Berfin：Springer，2009,529-538
[48] Zheng L，Wang Shaojun, Liu Yan, et a1．Information theoretic regularization forsemi-supervised boosting[C].ACM SIGKDD Conf KnowledgeDiscovery and DataMining．Paris，France：ACM Press，2009.1017-1026
[49] Mallapragada P. K.，Jin R，JainA.K., et al. SemiBoost：boosting for semi-supervisedlearning [J]．IEEE Trans Pattern Analysis and Machine Intelligence，2009，31(11)：2000-2014
[50] A Buades, B Coll, JM Morel. A review of image denoising algorithms with a new one[J]. Multiscale modeling and simulation.2005,4(2):490-530
[51] Julian St ttinger, Allan Hanbury, Nicu Sebe. Sparse color interest points for imageretrieval and object categorization[J]. IEEE Transactions on Image Processing.2012.21(5):2681-2692
[52]曾智勇，张学军，崔江涛.基于显著兴趣点颜色及空间分布的图像检索新方法[J].光子学报，2006，35(2):308-311
[53]孟繁杰，郭宝龙.一种基于兴趣点颜色及空间分布的图像检索方法[J].西安电子科技大学学报(自然科学版)，2005，32(2):256-259
[54]任桢.图像分类任务的关键技术研究[D].哈尔滨工程大学,博士学位论文,2010
[55] Wang Li-po,Rajapakse J C, Fukushima K. Speckle reduction using Wiener filtering inwavelet domain[C].Proceedings of the9th International Conference on NeuralInformation Processing,2002(2):792-795
[56] Tomasi C, Manduchi R. Bilateral filtering for gray and color images [C].Proceedingsof the6th International Conference on Computer Vision, Bombay,1998:8392846
[57]杨学志，徐勇，方静等，结合区域分割和双边滤波的图像去噪新算法[J],中国图象图形学报，2012,17(1):40-48
[58] A. Buades, B. Coll, and J. Morel. On image denoising methods[R]. CMLA,2004
[59] A. Buades, B. Coll, J. Morel. A nonlocal algorithm for image denoising[C]. ComputerVision and Pattern Recognition, CVPR2005. IEEE Computer Society Conference onVolume2, Page(s):60-65
[60]刘艳丽，郭延文，王进等,噪声方差未知的小波域中非局部均值图像去噪[J].计算机辅助设计与图形学报，2009.21(4):526-532
[61]包达,罗立民.基于可编程图形处理器的快速傅立叶变换[J].生物医学工程研究.2008,27(2):111-113
[62] Unser M., Aldroubi A., Laine, A.. Special Issue on Wavelets in Medical Imaging[J].IEEE Transactions on Medical Imaging.2003,22(3):285-288
[63]吴冬梅.基于平移不变小波阈值算法的噪音图像估计[J].光子学报，2005，34(2):306-309
[64] Chang G, Yu B, Vetterli M. Adaptive Wavelet Thresholding for Image Denoising andCompressiong [J]. IEEE Trans. On Image Proc.,2000,9(9):1532-1546
[65] Li Lin,Kong Lingfu. Image Denoising Based on Non-local Means with Wiener Filteringin Wavelet Domain[C]. Intelligent Information Hiding and Multimedia Signal Processing.IIH-MSP’09. Fifth International Conference.,2009.pp:471-474
[66] M. N. Do and M. Vetterli. The contourlet transform: an efficient directionalmultiresolution image representation [J]. Image Processing, IEEE Transactions on,2005.vol.14,pp.2091-2106
[67]李林,孔令富,练秋生.基于轮廓波维纳滤波的图像压缩传感重构[J].仪器仪表学报,30(10):2051-2056
[68]刘盛鹏，方勇.基于Contourlet变换和Wiener滤波的图像降噪[J].计算机工程，2008，34(5):210-212.
[69] CunhaA．L．，Zhou J．P．and Do M．N．The nonsubsampled contourlet transform：theory，design，and applications[J].IEEE Trans.Image Processing，2006,15(10):3089-3101
[70]边策,钟桦,焦李成.基于非下采用Contourlet变换和双变量模型的图像去噪[J].电子与信息学报,2009,31(3):561-565
[71]王志明，张丽.自适应的快速非局部图像去噪算法[J].中国图象图形学报.2009,14(4):669-675
[72] A. Dauwe, B. Goossens, H. Luong, and W. Philips. A fast non-local image denoisingalgorithm [C]. in Proceedings of SPIE Electronic Imaging,2008,vol.6812, pp.681210-681218
[73] Jin Wang, Yanwen Guo, Yiting Ying, Yanli Liu, Qunsheng Peng. Fast Non-LocalAlgorithm for Image Denoising [C]. IEEE ICIP06,2006, pp.1429-1432
[74] Dinesh peter. J, Govindan. V. K, Abraham T. Mathew. Robust Estimation Approach forNonlocal-means. Denoising Based On Structurally Similar Patches[J]. Int. J. OpenProblems Compt. Math., Vol.2, No.2, June2009,294-310
[75]戴维,于盛林,孙栓.基于Contourlet变换自适应阈值的图像去噪算法[J].电子学报,35(10):1939-1943
[76]董鸿燕，李吉成，沈振康.基于Contourlet变换的带噪图像增强方法[J].信号处理，2007，23(6):836-840
[77] Yuan X H，Buckles B P．Subband noise estimation for adaptive wavelet shrinkage[C].ICPR.Proceedings of the17th International Conference on PatternRecognition,2004,4:858-888
[78]杨芳宇，王向阳.一种基于边缘综合特征的彩色图像检索算法[J].计算机科学，2010，37(2):256-260
[79] Lew M S, Sebe N, Djeraba C, Jain R. Content-based multimedia information retrieval:state of the art and challenges[J].ACM Transactions on Multimedia Computing,Communications, and Applications,2006,2(1):1-19
[80] Stricker M，Dimai A.Color Indexing with Weak Spatial Constraints[C].Proc SPIE Confon Storage and Retrieval for Image and Video Databases IV,1996(26):29-40
[81] Li X L. Image retrieval based on perceptive weighted color blocks[J]. Pattern RecognitionLetters,2003,24(12):1935-1941
[82]王向阳，杨红颖，郑宏亮等，基于视觉权值的分块颜色直方图图像检索算法[J]，自动化学报，2010，10：1489-1492
[83] Rao A，Srihari R，Zhang Z．Spatial color histogram for content-based retrieval [C]. In11th IEEE International Conference on Tools with AI. California：IEEE ComputerSociety.1999,183-186
[84]孙君顶，毋小省.基于颜色分布特征的图像检索[J].光电子.激光，2006，17（8）：1009-1013
[85]邢强，袁保宗。唐晓芳．一种基于加权色彩直方图的快速图像检索方法[J]．计算机研究与发展，2005，42(11)：1903-1910
[86] H.W. Yoo, H.S. Park, and D.S. Jang, Expert system for color image retrieval[J]. ExpertSyst. Appl.,2005,28(2):347-357
[87] Smith J R，Chang S F．Tools and techniques for color image retrieval[C]．In：Sethi I K，Jain，R C，eds．Storage＆Retrieval for Image and Video Databases IV，IS&T／SPIEProceedings．1996，2670．426-437
[88]汪慧兰，赵海峰，罗斌．基于局部颜色空间特征的图像检索[J]．计算机技术与发展，2006，16(1):76-79
[89] L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid sceneanalysis[J]. IEEE TPAMI,1998,20(11):1254-1259
[90] W. Eihhauser and P. Konig. Does luminance-constrast contribute to a saliency map forovert visual attention?[J]. European Journal of Neuroscience,2003,17:1089-1097
[91] Achanta R, Hemami S, Estrada E. Salient region detection and segmentation [C].Proceedings of the6th International Conference on Computer Vision Systems，Santorini，2008:66-75
[92]胡正平，孟鹏权.基于空间分布特性和局部复杂度的显著目标检测算法[J],燕山大学报，2010，34（6）：521-527
[93]于永健，王向阳，基于颜色复杂度的加权颜色直方图图像检索算法[J],小型微型计算机系统，2009,30(3):507-511
[94]丁贵广，戴琼海，徐文立.基于兴趣点局部分布特征的图像检索方法[J].光电子.激光，2005，16(9):1101
[95]许相莉，基于智能计算的图像检索算法研究[D],吉林大学博士学位论文，2011，27-28
[96] Cinque L，Levialdi S，Olsen K，et al. Color-based image retrieval using spatial-chromatichistogram[C]．In Multimedia Computing and System，IEEE International Conferenceon.1999，2：969-973
[97] Q Zhou, L MA, M Celenk, et al. Content-based image retrieval based on ROI detectionand relevance feedback[J]. Multimedia Tools and Applications,2005,27(11):251-281
[98]沈兰荪，张菁，李晓光，图像检索与压缩域处理技术的研究[M].人民邮电出版社,2008
[99] A Bamidele, F Stentiford, J Morphett. An attention-based approach to content-basedimage retrieval[J]. BT Technology Journal.2004,22(7):151-160
[100] Fu H，Chi Z，Feng D．Attention driven image interpretation with application to imageretrieval[J]．Patern Recognition，2006，39(7)：1604-1621
[101] A A Salah, E Alpandin, L Akarn. A selective attention-based method for visual patternrecognition with application to handwritten digit recognition and face recognition[J].IEEETrans on Pattern Analysis and Machine Intelligence,2002,24(3):420-422
[102] I. A Rybak，V I Gusakova，A V Golovan，et al. A model of attention-guided visualperception and recognition[J], Vision Research,1998,vol.38, pp.2387-2400
[103] MA Y F, ZHANG H J. Contrast-based image attention analysis by using fuzzygrowing[C].Proceedings of the Eleventh ACM International Conference on Multimedia，New York：ACM Press,2003:374-381
[104] S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection[C]. InCVPR,2010,2376-2383
[105] R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient regiondetection[C]. In CVPR.2009,pages:1597-1604
[106]张菁，沈兰荪，高静静．基于视觉注意模型和进化规划的感兴趣区检测方法[J].电子与信息学报，2009，3l(7)：1646-1652
[107] Hou Xiaodi，Zhang Liqing．Saliency detection：a spectral residual approach[C]. IEEEConference on Computer Vision and Pattern Recognition，Minneapolis，2007:1-8
[108]张巧荣，顾国昌，刘海波等.利用多尺度频率分析的图像显著区域检测[J].哈尔滨工程大学学报，2010,31(3):261-365
[109]夏良正.数字图像处理[M].南京:东南大学出版社，1999:222-223，311-312
[110] Zhou X S, Huang T S. Edge-based structural features for content-based imageretrieval[J]. Pattern Recognition Letters,2001,22(5):457-468
[111] Kenneth R,Castleman.数字图像处理[M].北京：电子工业出版社，2002
[112]马岱农,桑海峰.基于边缘方向直方图的印鉴鉴别方法[J].辽宁科技大学学报，2010.33(3):258-261
[113] J. Harel, C. Koch, and P. Perona. Graph-based visual saliency[J].Advances in NeuralInformation Processing Systems,2007,19:545-552
[114] Datta Ritendra, LI Jia, Wang James z．Content based image retrieval approaches andtrends of the new Age[C]. Proceedings of the7th International Workshop on MultimediaInformation Retrieval in conjunction with ACM International Conference on Multimedia，Singapore，ACM, November2005：253-262
[115]高永英，章毓晋，罗云.基于目标语义特征的图像检索系统[J].电子与信息学报，2003，25(10)：1341-1348
[116] Eakins J, Graham M. Content-based image retrieval[R]. University of Northumbria atNewcastle,1999
[117]刘洁敏,姚豫,张瑞等.基于局部颜色-空间特征的图像语义概念检测[J].中国图象图形学报，2008，13(10)：1090-1893
[118]朱文球，刘强.一种新的图形语义自动标注与检索算法[J].计算机应用研究，2007，24(7)：318-320
[119] S. Kim, S. Park and M. Kim, Central object extraction for object-based imageretrieval[J], Int’l Conf. on Image and Video Retrieval.2003,pp39-49
[120] B. C. Ko and J.-Y. Nam. Object-of-interest image segmentation based on humanattention and semantic region clustering[J],Journal of Optical Society of America,2006,23(10):2462-2470
[121] Q.Z.Xia,X.Y.Feng and J.Y.Peng. Regions of Interest Detection with Edge and DepthInformation[J].Industrial Electronics and Applications,4th IEEECoference,2009.pp:3028-3031
[122] Chun-Ying Wu,Jin-Jang Leou,Hsuan-Ying Chen.Visual attention region determinationusing low-level features[J].Circuits and Systems,.IEEE international Symposium.2009:3178-3181
[123] E. Rosten and T. Drummond. Machine learning for high speed corner detection[J], InProc. of European Conference on Computer Vision,2006.volume1, pages:430-443
[124]王向阳，陈景伟，于永健.一种基于彩色边缘综合特征的图像检索算法[J],模式识别与人工智能,2010,23(2):216-221
[125] Ahonen T, Hadid A, Pietik ainen M. Face description with local binary patterns:application to face recognition[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,2006,28(12):2037-2041
[126] Guo Z H, Zhang L, Zhang D, Mou X Q. Hierarchical multiscale LBP for face andpalmprint recognition[C]. In Proc. of the16th International Conference on ImageProcessing.2010.4521-4524
[127] Heikkil a M, Pietik ainen M, Schmid C. Description of interest regions with localbinary patterns[J]. Pattern Recognition,2009,42(3):425-436
[128] Vapnik V N．Statistical learning theory[M]．John Wiley&Sons，NewYork，NY，1998
[129] Hsu C W, Lin C J. A comparison of methods for multi-class support vector machines[J].IEEE Transactions on Neural Networks2002,13(2):415-425
[2]庄越挺，潘云鹤，吴飞.网上多媒体信息分析与检索[M].北京:清华大学出版社，2002
[3] Datta R, Joshi D, Li J, Wang J Z. Image retrieval: ideas, influences, and trends of thenew age[J]. ACM Computing Surveys,2008,40(2):1-60
[4]章毓晋.基于内容的视觉信息检索[M].北京:科学出版社，2003
[5]安志勇.基于内容的图像检索关键技术研究[D].西安电子科技大学，博士学位论文，2008
[6]冯松鹤.面向感知的图像检索及自动化标注算法研究[D].北京交通大学，博士学位论文，2009
[7]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
[8] Yue J，Li Z B, Liu L,et a1.Content-based image retrieval using color and texture fusedfeatures[J].Mathematical and Computer Modelling,2011,54:1121-1127
[9] Yang N C, Chang W H. A fast MPEG-7dominant color extraction with new similaritymeasure for image retrieval[J]. Journal of Visual Communication&ImageRepresentation.2008,19(2):92-105
[10] Wang X Y, Yu Y J, Yang H Y. An effective image retrieval scheme using color, textureand shape features [J]. Computer Standards&Interfaces,2011,33:59-68
[11]黄元元，刘宁钟.一种新的基于颜色特征的图像检索方法[J].小型微型计算机系统，2012，33(3):609-613
[12]施智平,胡宏，李清勇等,基于纹理描述子的图像检索[J],软件学报,2005,16(6):1039-1045
[13] Young Deok Chun, Nam Chul Kim, Ick Hoon Jang. Content-based image retrieval usingmultiresolution color and texture features[J], IEEE Transactions on Multimedia,2008,10(6):1073-1084
[14] Wang H, Dai F, Zhang L, Lu S X．An image retrieval method based on texture featuresof object region[C]． International Conference on Electronics and Optoelectronics(ICEOE2011),4:83-86
[15] Zeng Zhiyong，Liu Shigang．A Novel Region-based Image Retrieval Algorithm UsingHybrid Feature[C].WRI World Congresson Computer Science and InformationEngineering．Los Angeles：IEEE Computer Society，2009：416-420
[16] Chiaug Cheng Chieh，Hung Yi Ping，Yang Hsuan，et a1．Region-based Image RetrievalUsing Color-size Features of Watershed Regions[J]．Journal of Visual Communicationand Image Representation，2009，20(3)：167-177
[17]王兵，张欣，王苗等.图像主题区域提取及其在图像检索中的应用[J].小型微型计算机系统，2011,32(3):567-571
[18] Hong, Fu, Zheru，Chi. An efficient algorithm for attention-driven image interpretationfrom segments [J].Pattern Recognition,2009:126-129
[19]斯白露，高文，卢汉清等．基于感兴趣区域的图像检索方法[J]．高技术通信，2003，13(5):13-18
[20]冯松鹤，郎丛妍，须德.一种融合图学习与区域显著性分析的图像检索算法[J].电子学报，2011，39(10)：2288-2294
[21]张菁，沈兰荪，David Dagan FENG．基于视觉感知的图像检索的研究[J].电子学报，2008，36(3):494-499
[22] J Zhang，L Zhuo，L S Shen. Regions of interest extraction based on visual attentionmodel and watershed segmentation [C]. IEEE International Conf. on Neural Networks&Signal Processing, China,2008,375-378
[23]李志欣，施智平，李志清，史忠植.图像检索中语义映射方法综述[J].计算机辅助设计与图形学学报，2008，20(8):1085-1094
[24] S.C.H. Hoi, R. Jin, J. Zhu, M.R. Lyu.Semisupervised SVM batch mode active learningwith applications to image retrieval[J]. ACM Transactions on InformationSystems,2009,27(3):1-29
[25] Carneiro G，Chan A B，Moreno P J，et a1．Supervised learning of semantic classes forimage annotation and retrieval[J]．IEEE Transactions on Pattern Analysis and MachineIntelligence，2007，29(3)：394-410
[26] Mohammed Belkhatir. A three-level architecture for bridging the image semantic gap[J].Multimedia Systems,2011,17:135-148
[27] Rasiwasia N，Moreno P J，Vasconcelos N．Bridging the gap：query by semantic example[J].IEEE Transactions on Multimedia，2007,9(5):923-938
[28] Gao Y I，Fan J P，Xue X Y，et a1． Automatic image annotation by incorporatingfeature hierarchy and boosting to scale up SVM classifiers[C].Proceedings of the14thACM International Conference on Multimedia.Santa Barbara.2006：901-9l0
[29] Zhao Y F， Zhao Y, Zhu Z F. Pseudo relevance feedback for search-based imageannotation [J]. Chinese Journal of Electronics.2008,17(3):471-474
[30] R.J. Liu, Y.H. Wang, SVM-based active feedback in image retrieval using clustering andunlabeled data[J]. Pattern Recognition,2008,41(8):2645-2655
[31] M.M. Rahman, B.C. Desai, A framework for medical image retrieval using machinelearning and statistical similarity matching techniques with relevance feedback[J], IEEETransactions on Information Technology in Biomedicine,2007,11(1):58-69
[32] Jun YANG, Shijiao ZHU, Daming LIU. An Image Annotation Method Using RelevanceFeedback Log and Semantic Network[J]. Journal of Computational Information Systems,2009,5(3):1331-1337
[33] Jing F，Li M J，Zhang H J，et a1．Aunified framework for image retrieval using keywordand visual features[J]．IEEE Transactions on Image Processing,2005,14(7):979-989
[34] Wang X Y, Chen J W, Yang H Y.A new integrated SVM classifiers for relevancefeedback content-based image retrieval using EM parameter estimation[J]. Applied SoftComputing,2011,11:2787-2804
[35] Shen，H.T.，B.C.Ooi，etal.，Giving meanings to www Images[C]. Proceedings of the8thACM International Conference on Multimedia (ACM MM),2000. Los Angeles,California:39-47
[36] Srihari，R.K.，Z.Zhang，etal.，Intelligent indexing and semantic retrieval of multimodaldocuments[J]. Information Retrieval,2000.2(2):245-275
[37]张华，张森，孟祥增.基于外部信息源的WWW图像语义提取研究[J].计算机科学，2006.33(4):211-214
[38]许相莉,张利彪,刘向东,于哲舟,周春光.基于粒子群的图像检索相关反馈算法[J].小型微型计算机系统.2010,38(8):1935-1940
[39] Deselaers T, Paredes R, Vidal E, Hermann N. Learning weighted distances for relevancefeedback in image retrieval[C]. In: Proceedings of19th International Conference onPattern Recognition, Tampa, FL, United stats,2008.1-4.
[40] Miguel Arevalillo-Herráez, Francesc J. Ferri, Salvador Moreno-Picot. Distance-basedrelevance feedback using a hybrid interactive genetic algorithm for image retrieval[J].Applied Soft Computing.2011,11:1782-1791
[41] Wu J, Fu Y, Lu M. Bayesian active learning in retrieval feedback for image retrieval[C].Proceedings of2nd international symposium on intelligent information technologyapplication.China:Inst. Of Elec,2008.371-375
[42] Ves E，Domingo J，et a1．Anovel Bayesian framework for relevance feedback in imagecontent-based retrieval system [J].Pattern Recognition,2006,39:1622-1632
[43]赵玉凤,赵耀,朱振峰.贝叶斯框架下基于区域的相关反馈算法[J].电子与信息学报,2008,30(4):937-940
[44] WANG B, ZHANG X,LI N. Relevance feedback technique for content-based imageretrieval using neural network learning[C].Proceedings of the5th internationalconference on Machine Learning and Cybernetics,2006,3692-3696
[45] Grigorova A, De Natale F G B, Dagli C, Huang T S. Content-based image retrieval byfeature adaption and relevance feedback [J]. IEEE transactions on multimedia,2007,9(6):1183-1192
[46]邬俊，鲁明羽，刘闯.基于混合学习框架的SVM反馈算法研究[J].电子学报.2010,38(9):2101-2106
[47] Zhou Z-H．When semi-supervised learning meets ensemble learning [C]．Proc IntWorkshop Multiple Classifier System．Berfin：Springer，2009,529-538
[48] Zheng L，Wang Shaojun, Liu Yan, et a1．Information theoretic regularization forsemi-supervised boosting[C].ACM SIGKDD Conf KnowledgeDiscovery and DataMining．Paris，France：ACM Press，2009.1017-1026
[49] Mallapragada P. K.，Jin R，JainA.K., et al. SemiBoost：boosting for semi-supervisedlearning [J]．IEEE Trans Pattern Analysis and Machine Intelligence，2009，31(11)：2000-2014
[50] A Buades, B Coll, JM Morel. A review of image denoising algorithms with a new one[J]. Multiscale modeling and simulation.2005,4(2):490-530
[51] Julian St ttinger, Allan Hanbury, Nicu Sebe. Sparse color interest points for imageretrieval and object categorization[J]. IEEE Transactions on Image Processing.2012.21(5):2681-2692
[52]曾智勇，张学军，崔江涛.基于显著兴趣点颜色及空间分布的图像检索新方法[J].光子学报，2006，35(2):308-311
[53]孟繁杰，郭宝龙.一种基于兴趣点颜色及空间分布的图像检索方法[J].西安电子科技大学学报(自然科学版)，2005，32(2):256-259
[54]任桢.图像分类任务的关键技术研究[D].哈尔滨工程大学,博士学位论文,2010
[55] Wang Li-po,Rajapakse J C, Fukushima K. Speckle reduction using Wiener filtering inwavelet domain[C].Proceedings of the9th International Conference on NeuralInformation Processing,2002(2):792-795
[56] Tomasi C, Manduchi R. Bilateral filtering for gray and color images [C].Proceedingsof the6th International Conference on Computer Vision, Bombay,1998:8392846
[57]杨学志，徐勇，方静等，结合区域分割和双边滤波的图像去噪新算法[J],中国图象图形学报，2012,17(1):40-48
[58] A. Buades, B. Coll, and J. Morel. On image denoising methods[R]. CMLA,2004
[59] A. Buades, B. Coll, J. Morel. A nonlocal algorithm for image denoising[C]. ComputerVision and Pattern Recognition, CVPR2005. IEEE Computer Society Conference onVolume2, Page(s):60-65
[60]刘艳丽，郭延文，王进等,噪声方差未知的小波域中非局部均值图像去噪[J].计算机辅助设计与图形学报，2009.21(4):526-532
[61]包达,罗立民.基于可编程图形处理器的快速傅立叶变换[J].生物医学工程研究.2008,27(2):111-113
[62] Unser M., Aldroubi A., Laine, A.. Special Issue on Wavelets in Medical Imaging[J].IEEE Transactions on Medical Imaging.2003,22(3):285-288
[63]吴冬梅.基于平移不变小波阈值算法的噪音图像估计[J].光子学报，2005，34(2):306-309
[64] Chang G, Yu B, Vetterli M. Adaptive Wavelet Thresholding for Image Denoising andCompressiong [J]. IEEE Trans. On Image Proc.,2000,9(9):1532-1546
[65] Li Lin,Kong Lingfu. Image Denoising Based on Non-local Means with Wiener Filteringin Wavelet Domain[C]. Intelligent Information Hiding and Multimedia Signal Processing.IIH-MSP’09. Fifth International Conference.,2009.pp:471-474
[66] M. N. Do and M. Vetterli. The contourlet transform: an efficient directionalmultiresolution image representation [J]. Image Processing, IEEE Transactions on,2005.vol.14,pp.2091-2106
[67]李林,孔令富,练秋生.基于轮廓波维纳滤波的图像压缩传感重构[J].仪器仪表学报,30(10):2051-2056
[68]刘盛鹏，方勇.基于Contourlet变换和Wiener滤波的图像降噪[J].计算机工程，2008，34(5):210-212.
[69] CunhaA．L．，Zhou J．P．and Do M．N．The nonsubsampled contourlet transform：theory，design，and applications[J].IEEE Trans.Image Processing，2006,15(10):3089-3101
[70]边策,钟桦,焦李成.基于非下采用Contourlet变换和双变量模型的图像去噪[J].电子与信息学报,2009,31(3):561-565
[71]王志明，张丽.自适应的快速非局部图像去噪算法[J].中国图象图形学报.2009,14(4):669-675
[72] A. Dauwe, B. Goossens, H. Luong, and W. Philips. A fast non-local image denoisingalgorithm [C]. in Proceedings of SPIE Electronic Imaging,2008,vol.6812, pp.681210-681218
[73] Jin Wang, Yanwen Guo, Yiting Ying, Yanli Liu, Qunsheng Peng. Fast Non-LocalAlgorithm for Image Denoising [C]. IEEE ICIP06,2006, pp.1429-1432
[74] Dinesh peter. J, Govindan. V. K, Abraham T. Mathew. Robust Estimation Approach forNonlocal-means. Denoising Based On Structurally Similar Patches[J]. Int. J. OpenProblems Compt. Math., Vol.2, No.2, June2009,294-310
[75]戴维,于盛林,孙栓.基于Contourlet变换自适应阈值的图像去噪算法[J].电子学报,35(10):1939-1943
[76]董鸿燕，李吉成，沈振康.基于Contourlet变换的带噪图像增强方法[J].信号处理，2007，23(6):836-840
[77] Yuan X H，Buckles B P．Subband noise estimation for adaptive wavelet shrinkage[C].ICPR.Proceedings of the17th International Conference on PatternRecognition,2004,4:858-888
[78]杨芳宇，王向阳.一种基于边缘综合特征的彩色图像检索算法[J].计算机科学，2010，37(2):256-260
[79] Lew M S, Sebe N, Djeraba C, Jain R. Content-based multimedia information retrieval:state of the art and challenges[J].ACM Transactions on Multimedia Computing,Communications, and Applications,2006,2(1):1-19
[80] Stricker M，Dimai A.Color Indexing with Weak Spatial Constraints[C].Proc SPIE Confon Storage and Retrieval for Image and Video Databases IV,1996(26):29-40
[81] Li X L. Image retrieval based on perceptive weighted color blocks[J]. Pattern RecognitionLetters,2003,24(12):1935-1941
[82]王向阳，杨红颖，郑宏亮等，基于视觉权值的分块颜色直方图图像检索算法[J]，自动化学报，2010，10：1489-1492
[83] Rao A，Srihari R，Zhang Z．Spatial color histogram for content-based retrieval [C]. In11th IEEE International Conference on Tools with AI. California：IEEE ComputerSociety.1999,183-186
[84]孙君顶，毋小省.基于颜色分布特征的图像检索[J].光电子.激光，2006，17（8）：1009-1013
[85]邢强，袁保宗。唐晓芳．一种基于加权色彩直方图的快速图像检索方法[J]．计算机研究与发展，2005，42(11)：1903-1910
[86] H.W. Yoo, H.S. Park, and D.S. Jang, Expert system for color image retrieval[J]. ExpertSyst. Appl.,2005,28(2):347-357
[87] Smith J R，Chang S F．Tools and techniques for color image retrieval[C]．In：Sethi I K，Jain，R C，eds．Storage＆Retrieval for Image and Video Databases IV，IS&T／SPIEProceedings．1996，2670．426-437
[88]汪慧兰，赵海峰，罗斌．基于局部颜色空间特征的图像检索[J]．计算机技术与发展，2006，16(1):76-79
[89] L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid sceneanalysis[J]. IEEE TPAMI,1998,20(11):1254-1259
[90] W. Eihhauser and P. Konig. Does luminance-constrast contribute to a saliency map forovert visual attention?[J]. European Journal of Neuroscience,2003,17:1089-1097
[91] Achanta R, Hemami S, Estrada E. Salient region detection and segmentation [C].Proceedings of the6th International Conference on Computer Vision Systems，Santorini，2008:66-75
[92]胡正平，孟鹏权.基于空间分布特性和局部复杂度的显著目标检测算法[J],燕山大学报，2010，34（6）：521-527
[93]于永健，王向阳，基于颜色复杂度的加权颜色直方图图像检索算法[J],小型微型计算机系统，2009,30(3):507-511
[94]丁贵广，戴琼海，徐文立.基于兴趣点局部分布特征的图像检索方法[J].光电子.激光，2005，16(9):1101
[95]许相莉，基于智能计算的图像检索算法研究[D],吉林大学博士学位论文，2011，27-28
[96] Cinque L，Levialdi S，Olsen K，et al. Color-based image retrieval using spatial-chromatichistogram[C]．In Multimedia Computing and System，IEEE International Conferenceon.1999，2：969-973
[97] Q Zhou, L MA, M Celenk, et al. Content-based image retrieval based on ROI detectionand relevance feedback[J]. Multimedia Tools and Applications,2005,27(11):251-281
[98]沈兰荪，张菁，李晓光，图像检索与压缩域处理技术的研究[M].人民邮电出版社,2008
[99] A Bamidele, F Stentiford, J Morphett. An attention-based approach to content-basedimage retrieval[J]. BT Technology Journal.2004,22(7):151-160
[100] Fu H，Chi Z，Feng D．Attention driven image interpretation with application to imageretrieval[J]．Patern Recognition，2006，39(7)：1604-1621
[101] A A Salah, E Alpandin, L Akarn. A selective attention-based method for visual patternrecognition with application to handwritten digit recognition and face recognition[J].IEEETrans on Pattern Analysis and Machine Intelligence,2002,24(3):420-422
[102] I. A Rybak，V I Gusakova，A V Golovan，et al. A model of attention-guided visualperception and recognition[J], Vision Research,1998,vol.38, pp.2387-2400
[103] MA Y F, ZHANG H J. Contrast-based image attention analysis by using fuzzygrowing[C].Proceedings of the Eleventh ACM International Conference on Multimedia，New York：ACM Press,2003:374-381
[104] S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection[C]. InCVPR,2010,2376-2383
[105] R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient regiondetection[C]. In CVPR.2009,pages:1597-1604
[106]张菁，沈兰荪，高静静．基于视觉注意模型和进化规划的感兴趣区检测方法[J].电子与信息学报，2009，3l(7)：1646-1652
[107] Hou Xiaodi，Zhang Liqing．Saliency detection：a spectral residual approach[C]. IEEEConference on Computer Vision and Pattern Recognition，Minneapolis，2007:1-8
[108]张巧荣，顾国昌，刘海波等.利用多尺度频率分析的图像显著区域检测[J].哈尔滨工程大学学报，2010,31(3):261-365
[109]夏良正.数字图像处理[M].南京:东南大学出版社，1999:222-223，311-312
[110] Zhou X S, Huang T S. Edge-based structural features for content-based imageretrieval[J]. Pattern Recognition Letters,2001,22(5):457-468
[111] Kenneth R,Castleman.数字图像处理[M].北京：电子工业出版社，2002
[112]马岱农,桑海峰.基于边缘方向直方图的印鉴鉴别方法[J].辽宁科技大学学报，2010.33(3):258-261
[113] J. Harel, C. Koch, and P. Perona. Graph-based visual saliency[J].Advances in NeuralInformation Processing Systems,2007,19:545-552
[114] Datta Ritendra, LI Jia, Wang James z．Content based image retrieval approaches andtrends of the new Age[C]. Proceedings of the7th International Workshop on MultimediaInformation Retrieval in conjunction with ACM International Conference on Multimedia，Singapore，ACM, November2005：253-262
[115]高永英，章毓晋，罗云.基于目标语义特征的图像检索系统[J].电子与信息学报，2003，25(10)：1341-1348
[116] Eakins J, Graham M. Content-based image retrieval[R]. University of Northumbria atNewcastle,1999
[117]刘洁敏,姚豫,张瑞等.基于局部颜色-空间特征的图像语义概念检测[J].中国图象图形学报，2008，13(10)：1090-1893
[118]朱文球，刘强.一种新的图形语义自动标注与检索算法[J].计算机应用研究，2007，24(7)：318-320
[119] S. Kim, S. Park and M. Kim, Central object extraction for object-based imageretrieval[J], Int’l Conf. on Image and Video Retrieval.2003,pp39-49
[120] B. C. Ko and J.-Y. Nam. Object-of-interest image segmentation based on humanattention and semantic region clustering[J],Journal of Optical Society of America,2006,23(10):2462-2470
[121] Q.Z.Xia,X.Y.Feng and J.Y.Peng. Regions of Interest Detection with Edge and DepthInformation[J].Industrial Electronics and Applications,4th IEEECoference,2009.pp:3028-3031
[122] Chun-Ying Wu,Jin-Jang Leou,Hsuan-Ying Chen.Visual attention region determinationusing low-level features[J].Circuits and Systems,.IEEE international Symposium.2009:3178-3181
[123] E. Rosten and T. Drummond. Machine learning for high speed corner detection[J], InProc. of European Conference on Computer Vision,2006.volume1, pages:430-443
[124]王向阳，陈景伟，于永健.一种基于彩色边缘综合特征的图像检索算法[J],模式识别与人工智能,2010,23(2):216-221
[125] Ahonen T, Hadid A, Pietik ainen M. Face description with local binary patterns:application to face recognition[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,2006,28(12):2037-2041
[126] Guo Z H, Zhang L, Zhang D, Mou X Q. Hierarchical multiscale LBP for face andpalmprint recognition[C]. In Proc. of the16th International Conference on ImageProcessing.2010.4521-4524
[127] Heikkil a M, Pietik ainen M, Schmid C. Description of interest regions with localbinary patterns[J]. Pattern Recognition,2009,42(3):425-436
[128] Vapnik V N．Statistical learning theory[M]．John Wiley&Sons，NewYork，NY，1998
[129] Hsu C W, Lin C J. A comparison of methods for multi-class support vector machines[J].IEEE Transactions on Neural Networks2002,13(2):415-425
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