基于内容的中草药植物图像检索关键技术研究
|
摘要
论文工作针对基于内容的中草药植物图像检索问题,在系统分析已有关键技术及发展趋势基础上,对中草药植物叶子图像的领域特征提取、层次化检索策略、基于显著轮廓曲线的图像检索、基于视觉注意的花卉图像检索等问题进行了系统、深入和较为全面的研究。这些研究内容不但是基于内容的中草药植物图像检索亟待解决的关键问题,也是图像处理和信息检索领域的研究重点,具有重要的理论意义和实际应用价值。论文的主要工作和贡献如下:
(1)对基于内容图像检索领域的一些关键技术作了深入的研究分析,包括:颜色、形状、纹理等常用的图像底层视觉特征和高层语义特征的描述,图像相似性度量准则,图像数据库特征索引,检索系统性能评价,相关反馈等;并对基于内容图像检索领域的主要研究方向进行了阐述;最后还给出了部分原型系统的比较分析结果。
(2)叶子作为植物的重要器官,它的识别与分类在整株植物的识别与分类过程中占有重要的地位。使用颜色、纹理、形状等通用的视觉特征并不能取得很好的检索效果,因此论文从植物形态学角度,分析并提取了中草药植物叶片的叶形、叶脉、叶齿等领域视觉特征,并且将所提取的特征归类为全局特征和局部特征,在此基础上,构建了一个层次化检索策略,并进行了实验分析。实验表明:应用领域特征的检索较传统的检索更有效,并且,层次化检索策略在提高系统检索速度的同时,又保证具有较高的检索精度。
(3)非标准环境下采集到的中草药植物叶子图像,一般具有复杂的背景,遮挡现象普遍存在,这都极大地影响着检索的效果。受到神经心理学中形状感知研究的启发,我们将非经典感受野抑制机制引入到图像边缘检测中,保留图像中叶子的轮廓,同时抑制复杂背景中的短小边缘,并且使用获取的轮廓曲线的特征来代表图像的形状特征。然后采用“综合多对多”的匹配策略来度量图像间的相似性,取得了良好的匹配效果。
(4)一般情况下,非标准环境下采集到的中草药植物花卉图像,花卉区域具有比背景更加突出的特征属性。利用人类视觉选择性注意机制研究的成果,首先对图像进行分析,综合视觉注意模型和传统的区域生长法,来定义和获取用户感兴趣的区域,然后采用一种新的“一对一”的匹配策略来度量图像间的相似性,解决了图像的注意性匹配问题。实验证明:上述方法简单有效,降低了信息处理的计算量,提高了系统的效率。
总之,我们在基于内容的中草药植物图像检索方面,首次运用比较先进的图像匹配与检索方法与技术,对中草药图谱检索问题做了有开拓性意义的研究工作,特别是提出的“植物叶子图像的领域特征提取与层次化检索”、“图像显著轮廓提取与综合轮廓匹配”、“基于视觉注意的感兴趣区域提取与花卉图像检索”等具体方法,对于推动中草药植物图像自动检索研究领域的技术发展,有着重要的学术价值和具体的应用意义。
This dissertation focuses on Content-based Chinese Herbal Medicine Botanic Image Retrieval (CBHBIR). Based on the rich introduction of some key techniques and the detailed analysis of future trends of Content-based Image Retrieval (CBIR), lots of exploratory research work has been investigated, which includes domain- related feature extraction of Chinese herbal medicine botanic images and hierarchical retrieval method, salient contour extraction and integrated contour matching, regions of interest (ROIs) extraction and matching based on visual attention model, and so on. The presented study is not only the key problems to be settled urgently in the CBHBIR field, but also the current research focus of image processing and information retrieval. Thus, these researches have both the academic and the applied significance.
The main contributions of this dissertation are summarized as follows:
(1) Several key techniques and algorithms of CBIR are deeply investigated and analyzed, such as, image content descriptors, similarity measures between images, indexical technologies, methods of performance evaluation and relevance feedback, etc. Moreover, the main research directions of CBIR are discussed. And several representative image retrieval systems are addressed.
(2) As an important organ of plants, leaf recognition and retrieval plays an important role in plant recognition and retrieval. Because of the inaccurate results retrieved by combining the general visual features, domain-related visual features are analyzed and extracted from the view of plant leaf morphology, such as leaf shape, leaf vein, leaf dent, etc. Owing to the different ability of distinguishing leaves, these features are classified into the global features and the local features. On such a basis, a hierarchical retrieval scheme for leaf images is brought forward. Experiments demonstrate that domain-related features can achieve better retrieval performance, and that the hierarchical retrieval scheme can increase the speed and precision of retrieval system.
(3) Chinese herbal medicine leaf images, taken in the natural environments, usually have cluttered background and the leaves partially occluded by other objects, which greatly affects the retrieval efficiency. With inspiration from psychophysical researches of the visual perception of shape, the mechanism of non-classical receptive field inhibition is introduced to contour detection. With such mechanism, the salient boundaries of the main leaves are retained while the short edges in the cluttered background are suppressed. According to the contour detection results, the salient boundaries of the image could be easily extracted, and the image features are thus described by those salient boundaries. Moreover, the integrated boundary matching strategy is adopted to measure the similarity between images. Experiment results show that the proposed methods have excellent retrieval performance.
(4) In general, for Chinese herbal medicine flower images, taken in the natural environments, the flower regions usually have prominent characteristic attributes. Motivated by the researches of visual selective attentive mechanism, images are analyzed and the extraction of ROIs is implemented by incorporating the visual attention model proposed by Itti with the seeded region growing. Images are matched by measuring the distances of the features of those ROIs. Experiment results show that the retrieval methods are simple and effective, which can greatly reduce the cost of information processing and increase the efficiency of retrieval system.
In a word, we have done some exploratory researches in the CBHBIR field by exploiting the advanced methods and technologies of image retrieval and have proposed some effective approaches which include the extraction of domain-related features and hierarchical retrieval scheme, the extraction of salient boundary and integrated boundary matching, the extraction and matching of ROIs based on visual attention model and so on. The researches presented in the dissertation will greatly accelerate the development of CBHBIR and have both academic and applied significance.
(1)对基于内容图像检索领域的一些关键技术作了深入的研究分析,包括:颜色、形状、纹理等常用的图像底层视觉特征和高层语义特征的描述,图像相似性度量准则,图像数据库特征索引,检索系统性能评价,相关反馈等;并对基于内容图像检索领域的主要研究方向进行了阐述;最后还给出了部分原型系统的比较分析结果。
(2)叶子作为植物的重要器官,它的识别与分类在整株植物的识别与分类过程中占有重要的地位。使用颜色、纹理、形状等通用的视觉特征并不能取得很好的检索效果,因此论文从植物形态学角度,分析并提取了中草药植物叶片的叶形、叶脉、叶齿等领域视觉特征,并且将所提取的特征归类为全局特征和局部特征,在此基础上,构建了一个层次化检索策略,并进行了实验分析。实验表明:应用领域特征的检索较传统的检索更有效,并且,层次化检索策略在提高系统检索速度的同时,又保证具有较高的检索精度。
(3)非标准环境下采集到的中草药植物叶子图像,一般具有复杂的背景,遮挡现象普遍存在,这都极大地影响着检索的效果。受到神经心理学中形状感知研究的启发,我们将非经典感受野抑制机制引入到图像边缘检测中,保留图像中叶子的轮廓,同时抑制复杂背景中的短小边缘,并且使用获取的轮廓曲线的特征来代表图像的形状特征。然后采用“综合多对多”的匹配策略来度量图像间的相似性,取得了良好的匹配效果。
(4)一般情况下,非标准环境下采集到的中草药植物花卉图像,花卉区域具有比背景更加突出的特征属性。利用人类视觉选择性注意机制研究的成果,首先对图像进行分析,综合视觉注意模型和传统的区域生长法,来定义和获取用户感兴趣的区域,然后采用一种新的“一对一”的匹配策略来度量图像间的相似性,解决了图像的注意性匹配问题。实验证明:上述方法简单有效,降低了信息处理的计算量,提高了系统的效率。
总之,我们在基于内容的中草药植物图像检索方面,首次运用比较先进的图像匹配与检索方法与技术,对中草药图谱检索问题做了有开拓性意义的研究工作,特别是提出的“植物叶子图像的领域特征提取与层次化检索”、“图像显著轮廓提取与综合轮廓匹配”、“基于视觉注意的感兴趣区域提取与花卉图像检索”等具体方法,对于推动中草药植物图像自动检索研究领域的技术发展,有着重要的学术价值和具体的应用意义。
This dissertation focuses on Content-based Chinese Herbal Medicine Botanic Image Retrieval (CBHBIR). Based on the rich introduction of some key techniques and the detailed analysis of future trends of Content-based Image Retrieval (CBIR), lots of exploratory research work has been investigated, which includes domain- related feature extraction of Chinese herbal medicine botanic images and hierarchical retrieval method, salient contour extraction and integrated contour matching, regions of interest (ROIs) extraction and matching based on visual attention model, and so on. The presented study is not only the key problems to be settled urgently in the CBHBIR field, but also the current research focus of image processing and information retrieval. Thus, these researches have both the academic and the applied significance.
The main contributions of this dissertation are summarized as follows:
(1) Several key techniques and algorithms of CBIR are deeply investigated and analyzed, such as, image content descriptors, similarity measures between images, indexical technologies, methods of performance evaluation and relevance feedback, etc. Moreover, the main research directions of CBIR are discussed. And several representative image retrieval systems are addressed.
(2) As an important organ of plants, leaf recognition and retrieval plays an important role in plant recognition and retrieval. Because of the inaccurate results retrieved by combining the general visual features, domain-related visual features are analyzed and extracted from the view of plant leaf morphology, such as leaf shape, leaf vein, leaf dent, etc. Owing to the different ability of distinguishing leaves, these features are classified into the global features and the local features. On such a basis, a hierarchical retrieval scheme for leaf images is brought forward. Experiments demonstrate that domain-related features can achieve better retrieval performance, and that the hierarchical retrieval scheme can increase the speed and precision of retrieval system.
(3) Chinese herbal medicine leaf images, taken in the natural environments, usually have cluttered background and the leaves partially occluded by other objects, which greatly affects the retrieval efficiency. With inspiration from psychophysical researches of the visual perception of shape, the mechanism of non-classical receptive field inhibition is introduced to contour detection. With such mechanism, the salient boundaries of the main leaves are retained while the short edges in the cluttered background are suppressed. According to the contour detection results, the salient boundaries of the image could be easily extracted, and the image features are thus described by those salient boundaries. Moreover, the integrated boundary matching strategy is adopted to measure the similarity between images. Experiment results show that the proposed methods have excellent retrieval performance.
(4) In general, for Chinese herbal medicine flower images, taken in the natural environments, the flower regions usually have prominent characteristic attributes. Motivated by the researches of visual selective attentive mechanism, images are analyzed and the extraction of ROIs is implemented by incorporating the visual attention model proposed by Itti with the seeded region growing. Images are matched by measuring the distances of the features of those ROIs. Experiment results show that the retrieval methods are simple and effective, which can greatly reduce the cost of information processing and increase the efficiency of retrieval system.
In a word, we have done some exploratory researches in the CBHBIR field by exploiting the advanced methods and technologies of image retrieval and have proposed some effective approaches which include the extraction of domain-related features and hierarchical retrieval scheme, the extraction of salient boundary and integrated boundary matching, the extraction and matching of ROIs based on visual attention model and so on. The researches presented in the dissertation will greatly accelerate the development of CBHBIR and have both academic and applied significance.
引文
1. 任廷革, 中医信息学. 2003, 中国:北京: 科学出版社.
2. 陈冲, 中药现代化研究. 2006, 中国:北京: 化学工业出版社.
3. 李文书, 中医舌诊中若干图像分析关键技术的研究. 2005, 浙江大学.
4. 韩军伟, 基于内容的图象检索技术研究. 2003, 西北工业大学.
5. M. Flickner, H.S., W. Niblack, J. Ashley, Q. Huang, B. Dom, et al., Query by Image and Video Content: QBIC System. IEEE Computer, 1995. 28(9): p. 23-32.
6. A. Pentland, R.W.P., S. Sclaroff, Photobook: Tools for Content-based Manipulation of Image Database. Proceedings of SPIE, 1994. Vol. 2185: p. 34-47.
7. J.R. Smith, S.C., VisualSEEk: A Fully Automated Content-based Image Query System. ACM Multimedia, 1996.
8. Ma W. Y., M.B.S., NETRA: A Toolbox for Navigating Large Image Databases. Proceeding of IEEE Conf. on Image Processing, 1997. 1: p. 568-571.
9. Carson C., B.S., Greenspan H., Malik J., BlobWorld: Image Segmentation Using Expectation-Maximization and Its Application to Image Query. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002. 24(8): p. 1026-1038.
10. J. Z. Wang, J.L., G. Wiederhold, SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001. 23(9): p. 1-17.
11. Y. Rui, T.S.H., S. Mehrotra, Relevance Feedback: A Powerful Tool in Interactive Content-based Image Retrieval. IEEE Trans. on Circuits and Systems for Video Technology, 1998. 8(5): p. 644-655.
12. T.S. Huang, S.M., K. Ramchanddran, Multimedia Analysis and Retrieval System(MARS) Project. Proceeding of 33rd Annual Clinic on Library Application of Data Processing Digital Image Access and Retrieval, 1996.
13. I.J. Cox, M.L.M., T.P. Minka, T.V. Papathomas, P.N. Yianilos, The Bayesian Image Retrieval System, PicHunter: Theory, Implementation And Psychophysical Experiments. IEEE Trans. on Image Processing, 2000. 9(1): p. 20-37.
14. T. P. Minka, R.W.P., Interactive Learning with A "Society of Models". Pattern Recognition, 1997. 30(4): p. 565-581.
15. Y. Ishikawa, R.S., C. Faloutsos, MindReader: Querying Databases through Multiple Example. Proceeding of VLDB, 1998.
16. 章毓晋, 图象工程(上册)——图像处理和分析. 1999, 中国:北京: 清华大学出版社.
17. B.M. Mehtre, M.S.K., A.D. Narasimhalu, Color Matching for Image Retrieval. Pattern Recognition Letters, 1995. 16(3): p. 325-331.
18. al, N.e., The QBIC Project: Querying Images by Content Using Color, Texture, and Shape. Proceeding of SPIE Conf. on Storage and Retrieval for Image and Video Databases, 1993.
19. Zhang H J, Z.D., A Scheme for Visual Feature-based Image Indexing. Proceeding of SPIE Conf. on Storage and Retrieval for Image and Video Databases, 1995.
20. M. J. Swain, D.H.B., Color Indexing. International Journal of Computer Vision, 1991. 7(1): p. 11-32.
21. M. Stricker, M.O., Similarity of Color Images. Proceeding of SPIE Conf. on Storage and Retrieval for Image and Video Databases, 1995. 2420: p. 381-392.
22. C.S. Fuh, S.W.C., K Essig, Hierarchical Color Image Region Segmantation for Content-based Image Retrieval System. IEEE Trans. on Image Processing, 2000. 9(1): p. 156-163.
23. H. Kauppinen, T.S., M. Pietikainen, An Experimental Comparison of Autoregressive and Fourier-based Descriptors in 2D Shape Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995. 17(2): p. 201-207.
24. MingKei, H., Visual Pattern Recognition by Moment Invariants. IEEETransaction on Information Theory, 1962. 8(2): p. 179-187.
25. Jacobs, C.E., Fast Multiresolution Image Querying. Proceeding of SIGGAPH, 1995: p. 277-286.
26. Haralick, R.M., Statictical and Structural Approaches to Texture. Proceedings of the IEEE, 1979. 67(5): p. 786-804.
27. Tamura H., e.a., Textural Features Corresponding to Visual Perception. IEEE Trans. on Systems, Man, and Cybernetics----Part B: Cybernetics, 1978. 8(6): p. 460-472.
28. M. Tuceryan, A.K.J., Texture Segmentation Using Voronoi Polygons. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990. 12: p. 211-216.
29. D. Blostein, N.A., Shape from Texture: Integrating Texture-element Extraction and Surface Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989. 11: p. 1233-1251.
30. Besag, J., Spatial Interaction and the Statictical Analysis of Lattice Systems. Journal of Royal Statistical Society, 1974. 36: p. 192-236.
31. Pentland, A., Fractal-based Description of Natural Scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984. 9: p. 661-674.
32. J. Mal, A.K.J., Texture Classification and Segmentation Using Multiresolution Simultaneous Autoregressive Models. Pattern Recognition, 1992. 25(2): p. 173-188.
33. J.M. Coggins, A.K.J., A Spatial Filtering Approach to Texture Analysis. Pattern Recognition Letters, 1995. 2: p. 195-203.
34. B.S. Manjunath, W.Y.M., Texture Features for Browsing and Retrieval of Image Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996. 18(8): p. 837-841.
35. Usnser, M., Texture Analysis and Segmentation Using Wavelet Frame. IEEE Trans. on Image Processing, 1995. 4(11): p. 1549-1561.
36. 王惠峰, 孙正兴, 王箭, 语义图像检索研究进展. 计算机研究与发展, 2002. 39(5): p. 513-523.
37. S. Santini, R.J., Similarity Match. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996. 18(9): p. 946-958.
38. T. Meiers, T.E., T. Sikora, Image Browsing with PCA-assisted User Interaction. Proceeding of IEEE Workshop on Content-based Access of Image and Video Libraries, 2001: p. 102-108.
39. A. Levey, M.L., Sequential Karhunen-Loeve Basis Extraction and Its Application to Images. IEEE Trans. on Image Processing, 2000. 9(8): p. 1371-1374.
40. 陈纯, 计算机图像处理技术与算法. 2003, 中国,北京: 清华大学出版社.
41. N. Beckmann, H.P.K., The R*-tree: An Efficient and Robust Access Method for Points and Rectangles. Proceeding of the ACM SIGMOD Confenrence on the Management of Data, 1990: p. 322-331.
42. D.A. White, R.J., Similarity Indexing with the SS-tree. Proceeding of International Conference on Data Engineering, 1996: p. 516-523.
43. G. H. Cha, C.W.C., The GC-tree: A High-dimensional Index Structure for Similarity Search in Image Database. IEEE Trans. on Multimedia, 2002. 4(2): p. 235-247.
44. Barros J., F.J., Martin W., Kelly P., Cannon M., Using the Triagle Inequality to Reduce the Number of Comparisons Required for Similarity-based Retrieval. SPIE Storage and Retrieval for Still Image and Video Database IV, 1996.
45. J.R. Smith, C.S.L., Image Retrieval Evaluation. Proceeding of Workshop on Content-based Access of Image and Video Libraries, 1998.
46. E. Ardizzone, M.C., Automatic Video Database Indexing and Retrieval. Multimedia Tools and Applications, 1997. 4(1): p. 29-56.
47. Y. Rui, T.S.H., Optimizing Learning in Image Retrieval. Proceedings of IEEE Conference Computer Vision and Pattern Recognition, 2000: p. 236-243.
48. Rocchio, J., Relevance Feedback in Information Retrieval. The Smart System-Experiments in Automatic Document Processing. 1971, Englewood Cliffs:NJ: Prcntice Hall. 313-323.
49. J. Huang, S.R.K., M. Mitra, Combining Supervised Learning with ColorCorrelograms for Content-based Image Retrieval. Proceeding of ACM Multimedia, 1997: p. 325-334.
50. C. Meilhac, C.N., Relevance Feedback and Category Search in Image Databases. IEEE Conference on Multimedia Computing and Systems, 1999.
51. A. Vailaya, A.K.J., H.J. Zhang, On Image Classification: City Images VS. Landscapes. Pattern Recognition, 1998. 31(12): p. 1921-1936.
52. N. Vasconcelos, A.L., A Bayesian Framework for Semantic Content Characterization. Proceeding of IEEE Conf. on CVPR, 1998.
53. F. Qian, M.J.L., L. Zhang, H.J. Zhang, B. Zhang, Gaussian Mixture Model for Relevance Feedback in Image Retrieval. Proceeding of IEEE Conf. on ICME, 2002.
54. X.S. Zhou, T.S.H., Small Sample Learning During Multimedia Retrieval Using BiasMap. Proceeding of IEEE Conf. on CVPR, 2001.
55. L. Zhang, F.Z.L., B. Zhang, Support Vector Machine Learning for Image Retrieval. Proceeding of IEEE Conf. on Image Processing, 2001.
56. S. Tong, E.C., Support Vector Machine Active Learning for Image Retrieval. Proceeding of ACM Multimedia, 2001.
57. K. Tier, P.V., Boosting Image Retrieval. Proceeding of CVPR, 2000.
58. C. Zhang, T.C., An Active Learning Framework for Content-based Information Retrieval. IEEE Trans. on Multimedia, 2002. 4(2): p. 260-268.
59. P.Hong, Q.T., T. Huang, Incorporate Support Vector Machines to Content-based Image Retrieval with Relevance Feedback. Proceeding of IEEE Conf. on ICIP, 2000.
60. al, R.B.J.e., The Virage Image Search Engine: An Open Framework for Image Management. Proceeding of SPIE Conf. on Storage and Retrieval for Image and Video Databases, 1996.
61. Deng Y., M.B.S., Unsupervised Segmentation of Color-Texture Regions in Images and Video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001. 23(8): p. 800-810.
62. 原襄, 植物观察入门. 1989: 渡假出版社.
63. 康延国, 中药鉴定学. 2005, 中国,北京: 中国中医药出版社.
64. Im C., N.H., Kunii T.L., Recognizing Plant Species by Leaf Shapes- a Case Study of the Acer Family. Proceeding of the 14th International Conference on Pattern Recognition, 1998. Vol. 2: p. 1171-1173.
65. Wang Z., C.Z., Feng D., Wang Q., Leaf Image Retrieval with Shape Features. Lecture Notes in Computer Science, 2000. Vol. 1929: p. 477-487.
66. Cunha, J.B., Application of image processing techniques in the characterization of plant leafs. Proceeding of 2003 IEEE International Symposium on Industrial Electronics, 2003. Vol. 1: p. 612-616.
67. 张帆, 张鸿宾. 基于内容的标准烟叶图像数据库检索. 计算机工程与应用, 2002. 38(7): p. 203-205.
68. 傅弘, 池哲儒, 常杰, 傅承新, 基于人工神经网络的叶脉信息提取——植物活体机器识别研究Ⅰ. 植物学通报, 2004. 21(4): p. 429-436.
69. 祁亨年, 植物外观特征自动获取及计算机辅助植物分类与识别. 浙江林学院学报, 2004. 21(2): p. 222-227.
70. Hussein, E., Nakamura, Y., Ohta, Y., Analysis of detailed patterns of contour shapes using wavelet local extrema. Proceedings of the 13th International Conference on Pattern Recognition, 1996. Vol. 2: p. 335-339.
71. 孙延奎, 小波分析及其应用. 2005, 中国,北京: 机械工业出版社.
72. Nakamura, Y., Yoshida, T., Learning two-dimensional shapes using wavelet local extrema. Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994. Vol. 3: p. 48-52.
73. 常杰, 陈刚, 葛滢, 植物结构的分形特征及模拟. 1995, 中国,杭州: 杭州大学出版社.
74. J, H.L., Leaf architectural classification of the angiosperms. Amer J Bot, 1971. 58: p. 469.
75. Ash A W, E.b., Hickey L J, Johnson K R, Wilf P, Wing S L, Manual of Leaf Architecture: Morphological Description and Categorization of Dicotyledonous and Net-Veined Monocotyledonous Angiosperms. 1999, Washington D C:Smithsonian Institution. 26-27.
76. X.S. Zhou, T.S.H., Edge-based structural features for content-based image retrieval. Pattern Recognition Letters, 2001. 22: p. 457-468.
77. JunWei Han, L.G., A shape-based image retrieval method using salient edges. Signal Processing: Image Communication, 2003. 18: p. 141-156.
78. TrunkGV, A Problem of Dimensionality: A simple Example. IEEE Transaction on PAMI, 1979. 1: p. 306-307.
79. Ripley, B.D., Pattern Recognition and Neural Networks. 1996: Cambridge University Press.
80. Bober, M., MPEG-7 visual shape descriptors. IEEE Trans. on Circuits and Systems for Video Technology, 2001. 11(6): p. 716-719.
81. Babu M. Mehtre, M.K., Wing Foon Lee, Shape Measures for Content Based Image Retrieval: A Comparison. Information Processing & Management, 1997. 33(3): p. 319-337.
82. John W. Gorman, O.R.M., Frank P. Kuhl, Partial Shape Recognition Using Dynamic Programming. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988. 10(2): p. 257-266.
83. Susan S. Young, P.D.S., Nasser M. Nasrabadi, Object Recognition Using Multilayer Hopfield Neural Network. IEEE Trans. on Image Processing, 1997. 6(3): p. 357-372.
84. N. Ansari, K.L., Landmark-based Shape Recognition by a modified Hopfield Neural Network. Pattern Recognition, 1993. 26: p. 531-542.
85. Anil K.Jain, Y.Z., et al, Object Matching Using Deformable Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996. 18(3): p. 267-277.
86. Alberto Del Bimbo, P.P., Visual Image Retrieval by Elastic Matching of User Sketches. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997. 19(2): p. 121-132.
87. Y.H. Gu, T.T., Corner-based Feature Extraction for Object Retrieval. Proceeding IEEE Conf. on Image Processing, 1999: p. 119-123.
88. A.K. Jain, A.V., Image Retrieval Using Color and Shape. Pattern Recognition, 1996. 29(8): p. 1233-1244.
89. Sikora, T., The MPEG-7 Visual Standard for content Description----An Overview. IEEE Trans. on Circuits and Systems for Video Technology, 2001. 11(6): p. 703-715.
90. R. Mehrotra, J.G., Similar-shape Retrieval in Shape Data Management. IEEE Computer, 1995. 28: p. 57-62.
91. Deliang W., D.T., Locally Excitatory Globally Inhibitory Oscillator Networks. IEEE Trans. On Neural Network, 1995. 6(1): p. 283-286.
92. S. Sarkas, K.L.B., Using Perceptual Inference Network to Manage Vision Processes. Computer Vision and Image Understanding, 1995. 62(1): p. 27-46.
93. Anarta Ghosh, N.P., Robustness of Shape Descriptors to Incomplete Contour Representations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005. 27(11): p. 1793-1804.
94. Gollin, E.S., Developmental Studies of Visual Recogniton of Incomplete Objects. Perceptual and Motor Skills 1960. 11: p. 289-298.
95. Y. Chans, Z.B.L., D. Lopresti, S.Y. Kung, A Feature-based approach for image retrieval by sketch. Proceeings of SPIE, 1998. 3430: p. 72-82.
96. Felice Andrea Pellegrino, W.V., Vincent Torre, Edge Detection Revisited. IEEE Trans. on Systems, Man, and Cybernetics----Part B: Cybernetics, 2004. 34(3): p. 1500-1518.
97. 徐科, 神经生物学纲要. 2001, 中国: 北京: 科学出版社.
98. J. G. Nicholls, A.R.M., B. G. Wallace, P. A. Fuchs, 神经生物学-----从神经元到脑. 4th ed. 2005, 中国:北京: 科学出版社.
99. C. Grigorescu, N.P., M. A. Westenberg, Contour and Boundary Detection Improved by Surround Suppression of Texture Edges. Image and Vision Computing, 2004. 22: p. 609-622.
100. J. P. Jones, L.A.P., An Evaluation of the Two-dimensional Gabor Filter Model of Simple Receptive Fields in Cat Striate Cortex. J. Neurophysiol., 1987. 58: p. 1233-1258.
101. Daugman, J.G., Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-dimensional Visual Cortical Filters. J. Opt. Soc. Amer: A, 1985. 2: p. 1160-1169.
102. Canny, J.F., A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986. 8(6): p. 679-698.
103. J. Li, J.Z.W., G. Wiederhold, IRM: Integrated Region Matching for Image Retrieval. Proceeding of ACM Multimedia, 2000: p. 147-156.
104. A. W. M. Smeulders, M.W., S. Santini, A. Gupta, R. Jain, Content-based Image Retrieval at the End of the Early Years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000. 22(12): p. 1349-1380.
105. G. Aggarwal, T.V.A., S. ghosal, An Image Retrieval System with Automatic Query Modification. IEEE Trans. on Multimedia, 2002. 4(2): p. 210-214.
106. F. Jing, M.J.L., H.J. Zhang, B. Zhang, An Efficient and Effective Region-Based Image Retrieval Framework. IEEE Trans. on Image Processing, 2004. 13(5): p. 699-710.
107. Moravec, H., Toward Automatic Visual Obstacle Avoidance. Proceeding of 5th International Joint Conf. on Artificial Intelligence, 1977: p. 584-590.
108. B. Moghaddam, H.b., D. Margaritis, Defining Image Content with Multiple Region-of-interest. Proceeding of IEEE Workshop on Content-based Access of Image and Video Libraries, 1999: p. 89-93.
109. C. Schmid, R.M., Local Grayvalue Invariants for Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997. 19(5): p. 530-535.
110. S. Bres, R.S., Detection of Interest Points for Image Indexation. Proceeding of IEEE Conf. on Image Processing, 1999: p. 227-234.
111. A. Heinrichs, D.K., B. Levienaise, Image Indexing and Content-based Search Using Pre-attentive Similarities. Proceeding of IEEE Conf. on Image Processing, 2000: p. 132-138.
112. Loupias E, S.N., Bres S, Jolion JM, Wavelet-based Salient Points for Image Retrieval. Proceedings of IEEE International Conf. on Image Processing, 2000.Vol 2: p. 518-521.
113. Halawani A, B.H., Image retrieval by local evaluation of nonlinear kernel functions around salient points. Proceedings of the 17th International Conference on Pattern Recognition, 2004. Vol 2: p. 955-960.
114. Zhang WY, T.J., Li C, The Extraction of Image's Salient Points for Image Retrieval. Proceeding of Fuzzy Systems and Knowledge Discovery, 2005: p. 547-556.
115. 徐斌, 兴趣点及灰度差分不变量在图像匹配中的应用研究. 2001, 西安电子科技大学: 陕西:西安.
116. C.M. Privitera, L.W.S., Algorithms for Defining Visual Regions-of-interest: Comparison with Eye Fixations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000. 22(9): p. 970-982.
117. L. Itti, C.K., E. Niebur, A Model of Saliency-based Visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998. 20(11): p. 1254-1259.
118. I. A. Rybak, V.I.G., A.V. Golovan, L.N. Podladchikova, N. A. Shevtsova, A Model of Attention-guided Visual Perception and Recognition. Vision Reserch, 1998. 38: p. 2387-2400.
119. Dimitri A. Chernyak, L.S., Top-down Guided Eye Movements. IEEE Trans. on Systems, Man, and Cybernetics----Part B: Cybernetics, 2001. 31(4): p. 510-522.
120. Tian M, L.S., Liao LZ, Zhao LW, Top-down attention guided object detection. Lecture Notes in Computer Science, 2006. 4232: p. 193-202.
121. L. Itti, C.K., Computational Modeling of Visual Attention. Nature Reviews Neuroscience, 2001. 2(3): p. 100-112.
122. L. Itti, C.K., A Comparison of Feature Combination Strategies for Saliency-based Visual Attention Systems. Proceeding of SPIE Human Vision and Electronic Imaging IV, 1999: p. 473-482.
123. Christopoulos C, A.J., Larsson M, Efficient methods for encoding regions of interest in the upcoming JPEG2000 still image coding standard. IEEE Signal Processing Letters, 2000. 7(9): p. 247-249.
124. Liu L, F.G., A New JPEG2000 Region-of-interest Image Coding Method: Partial Significant Bitplanes Shift. IEEE Signal Processing Letters, 2003. 10(2): p. 35-39.
125. Wang Z, B.A.C., Bitplane-by-bitplane Shift (BbBShift) - A Suggestion for JPEG2000 Region of Interest Image Coding. IEEE Signal Processing Letters, 2002. 9(5): p. 160-162.
126. A.A. Salah, E.A., L. Akarun, A Selective Attention-based Method for Visual Pattern Recognition with Application to Handwritten Digit Recognition and Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002. 24(3): p. 420-425.
127. Y.F. Ma, L.L., H.J. Zhang, M.J. Li, A User Attention Model for Video Summarization. proceeding of ACM Multimedia, 2002.
128. Chen LQ, X.X., Fan X, Ma WY, Zhang HJ, Zhou HQ, A Visual Attention Model for Adapting Images on Small Displays. Multimedia Systems, 2003. 9(4): p. 353-364.
129. 王润生, 图像理解. 1995, 湖南:长沙: 国防科学技术大学出版社.
130. N. R. Pal, S.K.P., A Review on Image Segmentation Techniques. Pattern Recognition, 1993. 26(9): p. 1277-1294.
131. Weska, J.S., A Survey of Threshold Selection Techniques. Computer Vision and Image Understanding, 1978. 7(2): p. 259-265.
132. Y. Ohta, T.K., T. Saki, Color Information for Region Segmentation. Computer Graphics and Image Processing, 1980. 13(3): p. 222-241.
133. Ramer, U., An Iterative Procedure for the Polygonal Approximation of Plane Curves. Computer Graphics and Image Processing, 1972. 1(3): p. 244-256.
134. J. Illingworth, J.K., A Survey of the Hough Transform. Computer Vision, Graphics, and Image Processing, 1988. 44(1): p. 87-116.
135. R. Adams, L.B., Seeded Region Growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994. 16(6): p. 641-647.
136. al., A.M.L.e., Evaluation of Methods for Ridge and Valley Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999. 21(4): p.327-335.
137. Pavlidis, T., Algorithms for Graphics and Image Processing. 1982, Rockville:MD: Computer Science Press.
138. J. P. Fan, D.K.Y.Y., A.K. Elmagarmid, W.G. Aref., Automatic Image Segmentation by Integrating Color-edge Extraction and Seeded Region Growing. IEEE Trans. on Image Processing, 2001. 10(10): p. 1454-1466.
139. S. A. Hojjatoleslami, J.K., Region Growing: A New Approach. IEEE Trans. on Image Processing, 1998. 7(7): p. 1079-1084.
2. 陈冲, 中药现代化研究. 2006, 中国:北京: 化学工业出版社.
3. 李文书, 中医舌诊中若干图像分析关键技术的研究. 2005, 浙江大学.
4. 韩军伟, 基于内容的图象检索技术研究. 2003, 西北工业大学.
5. M. Flickner, H.S., W. Niblack, J. Ashley, Q. Huang, B. Dom, et al., Query by Image and Video Content: QBIC System. IEEE Computer, 1995. 28(9): p. 23-32.
6. A. Pentland, R.W.P., S. Sclaroff, Photobook: Tools for Content-based Manipulation of Image Database. Proceedings of SPIE, 1994. Vol. 2185: p. 34-47.
7. J.R. Smith, S.C., VisualSEEk: A Fully Automated Content-based Image Query System. ACM Multimedia, 1996.
8. Ma W. Y., M.B.S., NETRA: A Toolbox for Navigating Large Image Databases. Proceeding of IEEE Conf. on Image Processing, 1997. 1: p. 568-571.
9. Carson C., B.S., Greenspan H., Malik J., BlobWorld: Image Segmentation Using Expectation-Maximization and Its Application to Image Query. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002. 24(8): p. 1026-1038.
10. J. Z. Wang, J.L., G. Wiederhold, SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001. 23(9): p. 1-17.
11. Y. Rui, T.S.H., S. Mehrotra, Relevance Feedback: A Powerful Tool in Interactive Content-based Image Retrieval. IEEE Trans. on Circuits and Systems for Video Technology, 1998. 8(5): p. 644-655.
12. T.S. Huang, S.M., K. Ramchanddran, Multimedia Analysis and Retrieval System(MARS) Project. Proceeding of 33rd Annual Clinic on Library Application of Data Processing Digital Image Access and Retrieval, 1996.
13. I.J. Cox, M.L.M., T.P. Minka, T.V. Papathomas, P.N. Yianilos, The Bayesian Image Retrieval System, PicHunter: Theory, Implementation And Psychophysical Experiments. IEEE Trans. on Image Processing, 2000. 9(1): p. 20-37.
14. T. P. Minka, R.W.P., Interactive Learning with A "Society of Models". Pattern Recognition, 1997. 30(4): p. 565-581.
15. Y. Ishikawa, R.S., C. Faloutsos, MindReader: Querying Databases through Multiple Example. Proceeding of VLDB, 1998.
16. 章毓晋, 图象工程(上册)——图像处理和分析. 1999, 中国:北京: 清华大学出版社.
17. B.M. Mehtre, M.S.K., A.D. Narasimhalu, Color Matching for Image Retrieval. Pattern Recognition Letters, 1995. 16(3): p. 325-331.
18. al, N.e., The QBIC Project: Querying Images by Content Using Color, Texture, and Shape. Proceeding of SPIE Conf. on Storage and Retrieval for Image and Video Databases, 1993.
19. Zhang H J, Z.D., A Scheme for Visual Feature-based Image Indexing. Proceeding of SPIE Conf. on Storage and Retrieval for Image and Video Databases, 1995.
20. M. J. Swain, D.H.B., Color Indexing. International Journal of Computer Vision, 1991. 7(1): p. 11-32.
21. M. Stricker, M.O., Similarity of Color Images. Proceeding of SPIE Conf. on Storage and Retrieval for Image and Video Databases, 1995. 2420: p. 381-392.
22. C.S. Fuh, S.W.C., K Essig, Hierarchical Color Image Region Segmantation for Content-based Image Retrieval System. IEEE Trans. on Image Processing, 2000. 9(1): p. 156-163.
23. H. Kauppinen, T.S., M. Pietikainen, An Experimental Comparison of Autoregressive and Fourier-based Descriptors in 2D Shape Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995. 17(2): p. 201-207.
24. MingKei, H., Visual Pattern Recognition by Moment Invariants. IEEETransaction on Information Theory, 1962. 8(2): p. 179-187.
25. Jacobs, C.E., Fast Multiresolution Image Querying. Proceeding of SIGGAPH, 1995: p. 277-286.
26. Haralick, R.M., Statictical and Structural Approaches to Texture. Proceedings of the IEEE, 1979. 67(5): p. 786-804.
27. Tamura H., e.a., Textural Features Corresponding to Visual Perception. IEEE Trans. on Systems, Man, and Cybernetics----Part B: Cybernetics, 1978. 8(6): p. 460-472.
28. M. Tuceryan, A.K.J., Texture Segmentation Using Voronoi Polygons. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990. 12: p. 211-216.
29. D. Blostein, N.A., Shape from Texture: Integrating Texture-element Extraction and Surface Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989. 11: p. 1233-1251.
30. Besag, J., Spatial Interaction and the Statictical Analysis of Lattice Systems. Journal of Royal Statistical Society, 1974. 36: p. 192-236.
31. Pentland, A., Fractal-based Description of Natural Scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984. 9: p. 661-674.
32. J. Mal, A.K.J., Texture Classification and Segmentation Using Multiresolution Simultaneous Autoregressive Models. Pattern Recognition, 1992. 25(2): p. 173-188.
33. J.M. Coggins, A.K.J., A Spatial Filtering Approach to Texture Analysis. Pattern Recognition Letters, 1995. 2: p. 195-203.
34. B.S. Manjunath, W.Y.M., Texture Features for Browsing and Retrieval of Image Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996. 18(8): p. 837-841.
35. Usnser, M., Texture Analysis and Segmentation Using Wavelet Frame. IEEE Trans. on Image Processing, 1995. 4(11): p. 1549-1561.
36. 王惠峰, 孙正兴, 王箭, 语义图像检索研究进展. 计算机研究与发展, 2002. 39(5): p. 513-523.
37. S. Santini, R.J., Similarity Match. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996. 18(9): p. 946-958.
38. T. Meiers, T.E., T. Sikora, Image Browsing with PCA-assisted User Interaction. Proceeding of IEEE Workshop on Content-based Access of Image and Video Libraries, 2001: p. 102-108.
39. A. Levey, M.L., Sequential Karhunen-Loeve Basis Extraction and Its Application to Images. IEEE Trans. on Image Processing, 2000. 9(8): p. 1371-1374.
40. 陈纯, 计算机图像处理技术与算法. 2003, 中国,北京: 清华大学出版社.
41. N. Beckmann, H.P.K., The R*-tree: An Efficient and Robust Access Method for Points and Rectangles. Proceeding of the ACM SIGMOD Confenrence on the Management of Data, 1990: p. 322-331.
42. D.A. White, R.J., Similarity Indexing with the SS-tree. Proceeding of International Conference on Data Engineering, 1996: p. 516-523.
43. G. H. Cha, C.W.C., The GC-tree: A High-dimensional Index Structure for Similarity Search in Image Database. IEEE Trans. on Multimedia, 2002. 4(2): p. 235-247.
44. Barros J., F.J., Martin W., Kelly P., Cannon M., Using the Triagle Inequality to Reduce the Number of Comparisons Required for Similarity-based Retrieval. SPIE Storage and Retrieval for Still Image and Video Database IV, 1996.
45. J.R. Smith, C.S.L., Image Retrieval Evaluation. Proceeding of Workshop on Content-based Access of Image and Video Libraries, 1998.
46. E. Ardizzone, M.C., Automatic Video Database Indexing and Retrieval. Multimedia Tools and Applications, 1997. 4(1): p. 29-56.
47. Y. Rui, T.S.H., Optimizing Learning in Image Retrieval. Proceedings of IEEE Conference Computer Vision and Pattern Recognition, 2000: p. 236-243.
48. Rocchio, J., Relevance Feedback in Information Retrieval. The Smart System-Experiments in Automatic Document Processing. 1971, Englewood Cliffs:NJ: Prcntice Hall. 313-323.
49. J. Huang, S.R.K., M. Mitra, Combining Supervised Learning with ColorCorrelograms for Content-based Image Retrieval. Proceeding of ACM Multimedia, 1997: p. 325-334.
50. C. Meilhac, C.N., Relevance Feedback and Category Search in Image Databases. IEEE Conference on Multimedia Computing and Systems, 1999.
51. A. Vailaya, A.K.J., H.J. Zhang, On Image Classification: City Images VS. Landscapes. Pattern Recognition, 1998. 31(12): p. 1921-1936.
52. N. Vasconcelos, A.L., A Bayesian Framework for Semantic Content Characterization. Proceeding of IEEE Conf. on CVPR, 1998.
53. F. Qian, M.J.L., L. Zhang, H.J. Zhang, B. Zhang, Gaussian Mixture Model for Relevance Feedback in Image Retrieval. Proceeding of IEEE Conf. on ICME, 2002.
54. X.S. Zhou, T.S.H., Small Sample Learning During Multimedia Retrieval Using BiasMap. Proceeding of IEEE Conf. on CVPR, 2001.
55. L. Zhang, F.Z.L., B. Zhang, Support Vector Machine Learning for Image Retrieval. Proceeding of IEEE Conf. on Image Processing, 2001.
56. S. Tong, E.C., Support Vector Machine Active Learning for Image Retrieval. Proceeding of ACM Multimedia, 2001.
57. K. Tier, P.V., Boosting Image Retrieval. Proceeding of CVPR, 2000.
58. C. Zhang, T.C., An Active Learning Framework for Content-based Information Retrieval. IEEE Trans. on Multimedia, 2002. 4(2): p. 260-268.
59. P.Hong, Q.T., T. Huang, Incorporate Support Vector Machines to Content-based Image Retrieval with Relevance Feedback. Proceeding of IEEE Conf. on ICIP, 2000.
60. al, R.B.J.e., The Virage Image Search Engine: An Open Framework for Image Management. Proceeding of SPIE Conf. on Storage and Retrieval for Image and Video Databases, 1996.
61. Deng Y., M.B.S., Unsupervised Segmentation of Color-Texture Regions in Images and Video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001. 23(8): p. 800-810.
62. 原襄, 植物观察入门. 1989: 渡假出版社.
63. 康延国, 中药鉴定学. 2005, 中国,北京: 中国中医药出版社.
64. Im C., N.H., Kunii T.L., Recognizing Plant Species by Leaf Shapes- a Case Study of the Acer Family. Proceeding of the 14th International Conference on Pattern Recognition, 1998. Vol. 2: p. 1171-1173.
65. Wang Z., C.Z., Feng D., Wang Q., Leaf Image Retrieval with Shape Features. Lecture Notes in Computer Science, 2000. Vol. 1929: p. 477-487.
66. Cunha, J.B., Application of image processing techniques in the characterization of plant leafs. Proceeding of 2003 IEEE International Symposium on Industrial Electronics, 2003. Vol. 1: p. 612-616.
67. 张帆, 张鸿宾. 基于内容的标准烟叶图像数据库检索. 计算机工程与应用, 2002. 38(7): p. 203-205.
68. 傅弘, 池哲儒, 常杰, 傅承新, 基于人工神经网络的叶脉信息提取——植物活体机器识别研究Ⅰ. 植物学通报, 2004. 21(4): p. 429-436.
69. 祁亨年, 植物外观特征自动获取及计算机辅助植物分类与识别. 浙江林学院学报, 2004. 21(2): p. 222-227.
70. Hussein, E., Nakamura, Y., Ohta, Y., Analysis of detailed patterns of contour shapes using wavelet local extrema. Proceedings of the 13th International Conference on Pattern Recognition, 1996. Vol. 2: p. 335-339.
71. 孙延奎, 小波分析及其应用. 2005, 中国,北京: 机械工业出版社.
72. Nakamura, Y., Yoshida, T., Learning two-dimensional shapes using wavelet local extrema. Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994. Vol. 3: p. 48-52.
73. 常杰, 陈刚, 葛滢, 植物结构的分形特征及模拟. 1995, 中国,杭州: 杭州大学出版社.
74. J, H.L., Leaf architectural classification of the angiosperms. Amer J Bot, 1971. 58: p. 469.
75. Ash A W, E.b., Hickey L J, Johnson K R, Wilf P, Wing S L, Manual of Leaf Architecture: Morphological Description and Categorization of Dicotyledonous and Net-Veined Monocotyledonous Angiosperms. 1999, Washington D C:Smithsonian Institution. 26-27.
76. X.S. Zhou, T.S.H., Edge-based structural features for content-based image retrieval. Pattern Recognition Letters, 2001. 22: p. 457-468.
77. JunWei Han, L.G., A shape-based image retrieval method using salient edges. Signal Processing: Image Communication, 2003. 18: p. 141-156.
78. TrunkGV, A Problem of Dimensionality: A simple Example. IEEE Transaction on PAMI, 1979. 1: p. 306-307.
79. Ripley, B.D., Pattern Recognition and Neural Networks. 1996: Cambridge University Press.
80. Bober, M., MPEG-7 visual shape descriptors. IEEE Trans. on Circuits and Systems for Video Technology, 2001. 11(6): p. 716-719.
81. Babu M. Mehtre, M.K., Wing Foon Lee, Shape Measures for Content Based Image Retrieval: A Comparison. Information Processing & Management, 1997. 33(3): p. 319-337.
82. John W. Gorman, O.R.M., Frank P. Kuhl, Partial Shape Recognition Using Dynamic Programming. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988. 10(2): p. 257-266.
83. Susan S. Young, P.D.S., Nasser M. Nasrabadi, Object Recognition Using Multilayer Hopfield Neural Network. IEEE Trans. on Image Processing, 1997. 6(3): p. 357-372.
84. N. Ansari, K.L., Landmark-based Shape Recognition by a modified Hopfield Neural Network. Pattern Recognition, 1993. 26: p. 531-542.
85. Anil K.Jain, Y.Z., et al, Object Matching Using Deformable Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996. 18(3): p. 267-277.
86. Alberto Del Bimbo, P.P., Visual Image Retrieval by Elastic Matching of User Sketches. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997. 19(2): p. 121-132.
87. Y.H. Gu, T.T., Corner-based Feature Extraction for Object Retrieval. Proceeding IEEE Conf. on Image Processing, 1999: p. 119-123.
88. A.K. Jain, A.V., Image Retrieval Using Color and Shape. Pattern Recognition, 1996. 29(8): p. 1233-1244.
89. Sikora, T., The MPEG-7 Visual Standard for content Description----An Overview. IEEE Trans. on Circuits and Systems for Video Technology, 2001. 11(6): p. 703-715.
90. R. Mehrotra, J.G., Similar-shape Retrieval in Shape Data Management. IEEE Computer, 1995. 28: p. 57-62.
91. Deliang W., D.T., Locally Excitatory Globally Inhibitory Oscillator Networks. IEEE Trans. On Neural Network, 1995. 6(1): p. 283-286.
92. S. Sarkas, K.L.B., Using Perceptual Inference Network to Manage Vision Processes. Computer Vision and Image Understanding, 1995. 62(1): p. 27-46.
93. Anarta Ghosh, N.P., Robustness of Shape Descriptors to Incomplete Contour Representations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005. 27(11): p. 1793-1804.
94. Gollin, E.S., Developmental Studies of Visual Recogniton of Incomplete Objects. Perceptual and Motor Skills 1960. 11: p. 289-298.
95. Y. Chans, Z.B.L., D. Lopresti, S.Y. Kung, A Feature-based approach for image retrieval by sketch. Proceeings of SPIE, 1998. 3430: p. 72-82.
96. Felice Andrea Pellegrino, W.V., Vincent Torre, Edge Detection Revisited. IEEE Trans. on Systems, Man, and Cybernetics----Part B: Cybernetics, 2004. 34(3): p. 1500-1518.
97. 徐科, 神经生物学纲要. 2001, 中国: 北京: 科学出版社.
98. J. G. Nicholls, A.R.M., B. G. Wallace, P. A. Fuchs, 神经生物学-----从神经元到脑. 4th ed. 2005, 中国:北京: 科学出版社.
99. C. Grigorescu, N.P., M. A. Westenberg, Contour and Boundary Detection Improved by Surround Suppression of Texture Edges. Image and Vision Computing, 2004. 22: p. 609-622.
100. J. P. Jones, L.A.P., An Evaluation of the Two-dimensional Gabor Filter Model of Simple Receptive Fields in Cat Striate Cortex. J. Neurophysiol., 1987. 58: p. 1233-1258.
101. Daugman, J.G., Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-dimensional Visual Cortical Filters. J. Opt. Soc. Amer: A, 1985. 2: p. 1160-1169.
102. Canny, J.F., A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986. 8(6): p. 679-698.
103. J. Li, J.Z.W., G. Wiederhold, IRM: Integrated Region Matching for Image Retrieval. Proceeding of ACM Multimedia, 2000: p. 147-156.
104. A. W. M. Smeulders, M.W., S. Santini, A. Gupta, R. Jain, Content-based Image Retrieval at the End of the Early Years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000. 22(12): p. 1349-1380.
105. G. Aggarwal, T.V.A., S. ghosal, An Image Retrieval System with Automatic Query Modification. IEEE Trans. on Multimedia, 2002. 4(2): p. 210-214.
106. F. Jing, M.J.L., H.J. Zhang, B. Zhang, An Efficient and Effective Region-Based Image Retrieval Framework. IEEE Trans. on Image Processing, 2004. 13(5): p. 699-710.
107. Moravec, H., Toward Automatic Visual Obstacle Avoidance. Proceeding of 5th International Joint Conf. on Artificial Intelligence, 1977: p. 584-590.
108. B. Moghaddam, H.b., D. Margaritis, Defining Image Content with Multiple Region-of-interest. Proceeding of IEEE Workshop on Content-based Access of Image and Video Libraries, 1999: p. 89-93.
109. C. Schmid, R.M., Local Grayvalue Invariants for Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997. 19(5): p. 530-535.
110. S. Bres, R.S., Detection of Interest Points for Image Indexation. Proceeding of IEEE Conf. on Image Processing, 1999: p. 227-234.
111. A. Heinrichs, D.K., B. Levienaise, Image Indexing and Content-based Search Using Pre-attentive Similarities. Proceeding of IEEE Conf. on Image Processing, 2000: p. 132-138.
112. Loupias E, S.N., Bres S, Jolion JM, Wavelet-based Salient Points for Image Retrieval. Proceedings of IEEE International Conf. on Image Processing, 2000.Vol 2: p. 518-521.
113. Halawani A, B.H., Image retrieval by local evaluation of nonlinear kernel functions around salient points. Proceedings of the 17th International Conference on Pattern Recognition, 2004. Vol 2: p. 955-960.
114. Zhang WY, T.J., Li C, The Extraction of Image's Salient Points for Image Retrieval. Proceeding of Fuzzy Systems and Knowledge Discovery, 2005: p. 547-556.
115. 徐斌, 兴趣点及灰度差分不变量在图像匹配中的应用研究. 2001, 西安电子科技大学: 陕西:西安.
116. C.M. Privitera, L.W.S., Algorithms for Defining Visual Regions-of-interest: Comparison with Eye Fixations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000. 22(9): p. 970-982.
117. L. Itti, C.K., E. Niebur, A Model of Saliency-based Visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998. 20(11): p. 1254-1259.
118. I. A. Rybak, V.I.G., A.V. Golovan, L.N. Podladchikova, N. A. Shevtsova, A Model of Attention-guided Visual Perception and Recognition. Vision Reserch, 1998. 38: p. 2387-2400.
119. Dimitri A. Chernyak, L.S., Top-down Guided Eye Movements. IEEE Trans. on Systems, Man, and Cybernetics----Part B: Cybernetics, 2001. 31(4): p. 510-522.
120. Tian M, L.S., Liao LZ, Zhao LW, Top-down attention guided object detection. Lecture Notes in Computer Science, 2006. 4232: p. 193-202.
121. L. Itti, C.K., Computational Modeling of Visual Attention. Nature Reviews Neuroscience, 2001. 2(3): p. 100-112.
122. L. Itti, C.K., A Comparison of Feature Combination Strategies for Saliency-based Visual Attention Systems. Proceeding of SPIE Human Vision and Electronic Imaging IV, 1999: p. 473-482.
123. Christopoulos C, A.J., Larsson M, Efficient methods for encoding regions of interest in the upcoming JPEG2000 still image coding standard. IEEE Signal Processing Letters, 2000. 7(9): p. 247-249.
124. Liu L, F.G., A New JPEG2000 Region-of-interest Image Coding Method: Partial Significant Bitplanes Shift. IEEE Signal Processing Letters, 2003. 10(2): p. 35-39.
125. Wang Z, B.A.C., Bitplane-by-bitplane Shift (BbBShift) - A Suggestion for JPEG2000 Region of Interest Image Coding. IEEE Signal Processing Letters, 2002. 9(5): p. 160-162.
126. A.A. Salah, E.A., L. Akarun, A Selective Attention-based Method for Visual Pattern Recognition with Application to Handwritten Digit Recognition and Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002. 24(3): p. 420-425.
127. Y.F. Ma, L.L., H.J. Zhang, M.J. Li, A User Attention Model for Video Summarization. proceeding of ACM Multimedia, 2002.
128. Chen LQ, X.X., Fan X, Ma WY, Zhang HJ, Zhou HQ, A Visual Attention Model for Adapting Images on Small Displays. Multimedia Systems, 2003. 9(4): p. 353-364.
129. 王润生, 图像理解. 1995, 湖南:长沙: 国防科学技术大学出版社.
130. N. R. Pal, S.K.P., A Review on Image Segmentation Techniques. Pattern Recognition, 1993. 26(9): p. 1277-1294.
131. Weska, J.S., A Survey of Threshold Selection Techniques. Computer Vision and Image Understanding, 1978. 7(2): p. 259-265.
132. Y. Ohta, T.K., T. Saki, Color Information for Region Segmentation. Computer Graphics and Image Processing, 1980. 13(3): p. 222-241.
133. Ramer, U., An Iterative Procedure for the Polygonal Approximation of Plane Curves. Computer Graphics and Image Processing, 1972. 1(3): p. 244-256.
134. J. Illingworth, J.K., A Survey of the Hough Transform. Computer Vision, Graphics, and Image Processing, 1988. 44(1): p. 87-116.
135. R. Adams, L.B., Seeded Region Growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994. 16(6): p. 641-647.
136. al., A.M.L.e., Evaluation of Methods for Ridge and Valley Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999. 21(4): p.327-335.
137. Pavlidis, T., Algorithms for Graphics and Image Processing. 1982, Rockville:MD: Computer Science Press.
138. J. P. Fan, D.K.Y.Y., A.K. Elmagarmid, W.G. Aref., Automatic Image Segmentation by Integrating Color-edge Extraction and Seeded Region Growing. IEEE Trans. on Image Processing, 2001. 10(10): p. 1454-1466.
139. S. A. Hojjatoleslami, J.K., Region Growing: A New Approach. IEEE Trans. on Image Processing, 1998. 7(7): p. 1079-1084.