一种基于图元的多级图像检索系统
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摘要
随着多媒体、网络技术的飞速发展，图像的应用日益广泛，大量的图像数据成倍增长，如何有效的管理和检索图像，已经成为人们迫切需要解决的问题，图像检索技术正成为国内外研究的一个热点。图像检索技术结合了图像处理、模式识别、图像理解、人工智能、机器视觉、数据库等技术，是一项在理论研究和实际应用中都极有前途的新技术。
基于文本的图像检索和基于内容的图像检索是目前图像检索的两种主要方法。其中基于文本的图像检索方法利用人工对图像进行标注作为检索特征，进行的是精确匹配；而基于内容的图像检索方法则是通过机器自动提取图像的内容(如颜色、纹理、形状、对象和空间关系等)作为检索特征，并利用相似性度量算法进行的近似匹配。基于文本的图像检索方法早在上个世纪七十年代就开始进行研究，其技术现在已经相对成熟；而基于内容的图像检索研究是上个世纪九十年代才开始兴起，它主要解决的是文本检索中人工标注所带来的一系列问题，作为一门新技术正在快速发展中。
本文叙述了整个图像检索技术的发展历史和现状，重点介绍了基于内容的图像检索技术的各个方面，深入分析了目前两种图像检索技术存在的问题和各自的优缺点，在此基础上提出了一种基于图元的多级图像检索系统。该系统通过搭建“语义
太原理工大学硕士研究生学位论文
词典”和“图元特征库”两个模块，对基于文本的图像检索和
基于内容的图像检索技术进行了有机的融合，充分发挥了两者
的优势，使图像底层视觉特征和图像高层语义特征之间建立起
关联，初步解决了人的高层语义理解和图像底层视觉特征之间
存在的“语义鸿沟”问题，在现有技术水平上满足了人们语义
检索的需要，是对图像语义检索技术研究的有益尝试。
With the development of the multimedia and network technology, the application of the image is extensive and the content-based image retrieval technique has already become the studied focus. It has combined the technologies, such as image processing, pattern-recognition, vision understanding, artificial intelligence, computer vision, databases, etc. It is an extremely promising new technology in research and application.
The text-based image retrieval and content-based image retrieval technique are two kinds of main methods to image retrieval at present. The text-based image retrieval technique utilizes the manual image annotation as image character for retrieval and is a precise matching. The content-based image retrieval utilizes computer to automatically extract image content (like color, texture, shape, object and space-relation, etc) as image character and is a similarity measure. The text-based image retrieval can be traced back to the late 1970's and its technology is already mature. But the content-based image retrieval what proposed in the early 90's and in process of speediness
development is used to solve a series of problems brought by manual image annotation in the text-based image retrieval.
In this paper we narrate the developing history of image retrieval technique and current situation, especially introduce all respects of the content-based image retrieval technique, deeply analyse existing problems and each advantage or defect in two kinds of image retrieval technique and have proposed a primitive-based multistage image retrieval system on the basis. Through build the "semantic dictionary" module and the " primitive-feature base" module in retrieval system, have finished the organic integration of two kinds of retrieval methods. In this system, we set up the relationship between vision character of ground floor of image and semantic character on the senior level of image, and primarily solve "semantic gap" between the simple visual characters and the abundant semantics delivered by an image. On the existing technical level, it can meet the need of people about semantic image retrieval and it is a benefic attempt to semantic image retrieval technique.
基于文本的图像检索和基于内容的图像检索是目前图像检索的两种主要方法。其中基于文本的图像检索方法利用人工对图像进行标注作为检索特征，进行的是精确匹配；而基于内容的图像检索方法则是通过机器自动提取图像的内容(如颜色、纹理、形状、对象和空间关系等)作为检索特征，并利用相似性度量算法进行的近似匹配。基于文本的图像检索方法早在上个世纪七十年代就开始进行研究，其技术现在已经相对成熟；而基于内容的图像检索研究是上个世纪九十年代才开始兴起，它主要解决的是文本检索中人工标注所带来的一系列问题，作为一门新技术正在快速发展中。
本文叙述了整个图像检索技术的发展历史和现状，重点介绍了基于内容的图像检索技术的各个方面，深入分析了目前两种图像检索技术存在的问题和各自的优缺点，在此基础上提出了一种基于图元的多级图像检索系统。该系统通过搭建“语义
太原理工大学硕士研究生学位论文
词典”和“图元特征库”两个模块，对基于文本的图像检索和
基于内容的图像检索技术进行了有机的融合，充分发挥了两者
的优势，使图像底层视觉特征和图像高层语义特征之间建立起
关联，初步解决了人的高层语义理解和图像底层视觉特征之间
存在的“语义鸿沟”问题，在现有技术水平上满足了人们语义
检索的需要，是对图像语义检索技术研究的有益尝试。
With the development of the multimedia and network technology, the application of the image is extensive and the content-based image retrieval technique has already become the studied focus. It has combined the technologies, such as image processing, pattern-recognition, vision understanding, artificial intelligence, computer vision, databases, etc. It is an extremely promising new technology in research and application.
The text-based image retrieval and content-based image retrieval technique are two kinds of main methods to image retrieval at present. The text-based image retrieval technique utilizes the manual image annotation as image character for retrieval and is a precise matching. The content-based image retrieval utilizes computer to automatically extract image content (like color, texture, shape, object and space-relation, etc) as image character and is a similarity measure. The text-based image retrieval can be traced back to the late 1970's and its technology is already mature. But the content-based image retrieval what proposed in the early 90's and in process of speediness
development is used to solve a series of problems brought by manual image annotation in the text-based image retrieval.
In this paper we narrate the developing history of image retrieval technique and current situation, especially introduce all respects of the content-based image retrieval technique, deeply analyse existing problems and each advantage or defect in two kinds of image retrieval technique and have proposed a primitive-based multistage image retrieval system on the basis. Through build the "semantic dictionary" module and the " primitive-feature base" module in retrieval system, have finished the organic integration of two kinds of retrieval methods. In this system, we set up the relationship between vision character of ground floor of image and semantic character on the senior level of image, and primarily solve "semantic gap" between the simple visual characters and the abundant semantics delivered by an image. On the existing technical level, it can meet the need of people about semantic image retrieval and it is a benefic attempt to semantic image retrieval technique.
引文
[1] J R Smith and S F Chang. VisualSEEK: a fully automated content-based image query system. ACM Multimedia 96, Boston, MA, Nov. 1996.
[2] M Ioka. A method of defining the similarity of image on the basis of color information. Technical Report RT-0030, IBM Research, November 1989.
[3] M J Swain and D H Ballard. Color indexing. International Journal of Computer Vision, Vol. 7, No. 1, pp. 11-32, 1991.
[4] Y Gong, H J Zhang and T C Chua. An image database system with content capturing and fast image indexing abilities. Proc. IEEE International Conference on Multimedia Computing and Systems, Boston, 14-19 May 1994, pp. 121-130.
[5] M Stricker and M Orengo. Similarity of color image, SPIE Storage and Retrieval for Image and Video Database Ⅲ, vol. 2185, pp. 381-392, Feb. 1995.
[6] G Pass and R Zabih. Histogram refinement for content-based image retrieval. IEEE Workshop on Applications of Computer Vision, pp. 96-102, 1996.
[7] W Y Ma and B S Manjunath. Edge flow: a framework of boundary detection and image segmentation. IEEE Int. Conf on Computer Vision and Pattern Recognition. pp.744-749, Puerto Rico, June 1997.
[8] John R Smith and Shih-Fu Chang. Automated binary texture feature sets for image retrieval. In Proc. IEEE Int. Conf. Acoust, Speech, and Signal Proc., May 1996.
[9] Robert M Haralick, K Shanmugam, and Its'hak Dinstein. Textnre features for image classification. IEEE Trans. On Sys, Man, and Cyb, SMC-3(6):610-621, 1973.
[10] H Tamura, S Mori and T Yamawaki. Texture features corresponding to visual perception. IEEE Trans. On Systems, Man, and Cybernetics, vol. Smc-8, no.6, June 1978.
[11] Niblack, et al. The QBIC project: querying images by content using color, texture, and shape. Proc. Of SPIE, Storage and Retrieval for Image and Video Databases, Vol. 1908, February 1993, San Jose. pp. 173-187.
[12] 郑南宁．计算机视觉与模式识别．国防工业出版社，1998年．
[13] J Mao and A K Jain. Texture classification and segmentation using muitiresolution simultaneous autoregressive models. Pattern Recognition, Vol. 25, No. 2, pp. 173-188, 1992.
[14] R W Picard, T Kabir and F Liu. Real-time recognition with the entire Brodatz texture database. Proc. IEEE Int, Conf. on Computer Vision and Pattern Recognition, pp. 638-639, New York, June 1993.
[15] B S Manjunath and W Y Ma. Texture features for browsing and retrieval of image
data. IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 18, No.8, pp.837-842, Aug. 1996.
[16] T Chang and C C Jay Kuo. Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. On Image Processing, vol. 2, no. 4, pp. 429-441, October 1993.
[17] A Laine and J Fan. Texture classification by wavelet packet signatures. IEEE Trans. Pattern Analysis and Machin Intelligence, vol. 15. 11, pp. 1186-1191, Nov. 1993.
[18] W Y Ma and B S Manjunath. A comparison of wavelet features for texture annotation. Proc. of IEEE Int. Conf. on Image Processing, vol. Ⅱ, pp. 256-259, Washington D. C., Oct. 1995.
[19] M K Hu. Visual pattern recognition by moment invariants, in J. K. Aggarwal, R. O.Duda, and A. Rosenfeld, Computer Methods in Image analysis, IEEE computer Society, Los Angeles, CA, 1977.
[20] A K Jain. Fundamentals of Digital Image Processing. Englewood Cliffs, Prentice Hall, 1989.
[21] L Yang and F Algregtsen. Fast computation of invariant geometric moments: A new method giving correct results. Proc. IEEE Int. Conf. On Image Processing, 1994.
[22] Deepak Kapur, Y N Lakshman and Tushar Saxena. Computing invariants using elimination methods. In Proc. IEEE Int. Conf. on Image roc, 1995.
[23] David Copper and Zhibin Lei. On representation and invariant recognition of complex objects based on patches and parts. In Spinger Lecture Notes in Computer Science series, 3D Object Representation for Computer Vision. M. Hebert, J. Ponce, T. Boult, A. Gross, Eds., New York,: Springer, 1995, pp. 139-153.
[24] S-K Chang, Q Y Shi and C Y Yan. Iconic indexing by 2-D strings. IEEE Trans. Pattern Anal. Machine Intell., 9(3),pp. 413-428, May 1987.
[25] V H Gudivada and V V Raghavan. Design and evaluation of algorithms for image retrieval by spatial similarity. ACM Trans. On Information Systems, vol. 13, no. 2.pp. 115-144, April 1995.
[26] M Lybanon, S Lea and S Himes. Segmentation of diverse image types using opening and closing, In Proc. IEEE Int. Conf. on Image Proc. 1994.
[27] X Q Li, Z W Zhao, H D Cheng, C M Huang and R W Harris. A fuzzy logic approach to image segmentation. In Proc. IEEE Int. Conf. on Image Proc. 1994.
[28] Ramin Samadani and Cecilia Han. Computer-assisted extraction of boundaries from image. In Proc. SPIE Storage and Retrieval for Image and Video Databases, 1993.
[29] Dirk Daneels, D Campenhout, Wayne Niblack, Will Equitz, Ron Barber, Erwin Bellon, and Freddy Fierens. Interactive outlining: An improved approach using active contours. In Proc. SPIE Storage and Retrieval for Image and Video Databases, 1993.
[30] Yong Rui, Alfred C She and Thomas S Huang. Automated shape segmentation using
attraction-based grouping in spatial-color-texture space. In Proc. IEEE Int. Conf. on Image Proc., 1996.
[31] A Celentano, V D Lecce. A FFT based technique for image signature generation[A]. Proc SPIE: Storage and Retrieval for Image and Video Databases V 3022[C]. Newport Beach CA USA: 1997.457-466.
[32] R Brunelli, O Mich. Image retrieval by examples[J]. IEEE Transaction on Multimedia, 2000, 2(3): 164-171.
[33] 魏海，沈兰荪．小波变换域内基于方向梯度相角直方图的图像检索算法[J]．电路与系统学报，2001，6(2)：20-24．
[34] R Reevesm, K Kubik, W Osberger. Texture characterization of compressed aerial images using DCT coefficients[A]. Proc. SPIE: Storage and Retrieval for Image and Video Databases V3022[C]. Newport Beach CA USA: 1997, 398-407.
[35] M Shneier, M S Mottaleb. Exploiting the JPEG compression scheme for image retrieval[J]. IEEE Trans, Pattern Analysis and Machine intelligence, 1996, 18(8): 849-853.
[36] S. K. Chang, Iconic indexing by 2D string[J]. IEEE Trans pattern analysis and machine intelligence, 1984, 6(4): 413-428.
[37] T Chang, C C Kuo. Texture analysis and classification with tree-structured wavelet transform[J]. IEEE Trans Image Processing, 1993, 2(4): 429-441.
[38] M K Mandal, T Aboulnasr, S Panchanathan. Image indexing using moments and wavelets[J]. IEEE Trans Consumer Electronics, 1996, 42(3): 557-565.
[39] J L Chen, A Kundu. Rotation and gray scale invariant texture identification using wavelet decomposition and hidden Markov model[J]. IEEE Trans Pattern Analysis and Machine Intelligence, 1994, 16(2): 208-214.
[40] M K Mandal, F Idris, S Panchanathan. A critical evaluation of image and video indexing techniques in the compressed domain[J]. Image and Vision Computing, 1999, 17:513-529.
[41] F Idris, S Panchanathan. Storage and retrieval of compressed images[J]. IEEE Trans Consumer Electronics, 1995, 41: 937-941.
[42] T Ida, Y Sambonsugi. Image segmentation using fractal coding[J]. IEEE Trans Circus and Systems for Video Technology, 1995, 5: 567-570.
[43] F Idris, S Panchanathan. Image indexing using wavelet vector quantization[A]. Digital Linage Storage Archiving Systems[C]. Philadelpia Pa USA: SPIE Proceedings, 1995.
[44] H J Zhang and D Zhong. A Scheme for visual feature-based image indexing. Proc.of SPIE conf. on Storage and Retrieval for Image and Video Databases Ⅲ,pp.36-46,Sam Jose, Feb. 1995.
[45] Fred Attneave. Dimensions of Similarity. American Journal of Psychology,
63:516-556,1950.
[46] Tversky, Amos(1977). Features of Similarity. Psychological Review, 84-4,pp.327-352.
[47] 樊玮虹，薛峰等．一种用于图像检索的聚类方法．计算机工程，200，26(2)：28～29．
[48] 伯晓晨，刘建平．基于颜色直方图的图像检索．中国图像图形学报．1999，4(1)：33～37．
[49] D White and R Jain. Similarity indexing: Algorithms and Performance. Proc. SPIE Storage and Retrieval for Image and Video Database, 1996.
[50] R Ng and A Sedighian. Evaluating Multi-dimensional Indexing Structures for Images Transformed by Principal Component Analysis. Proc. SPIE Storage and Retrieval for Image and Video Database, 1996.
[51] Rui Y, Huang T S, Ortega M. Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Trans on Circuits and Video Technology, 1998, 8(5)
[52] Rui, Y, Huang, T S, Mehrotra, S. Content-based Image Retrieval with Relevance Feedback in MARS. in Proc. IEEE Int. Conf. On Image Processing. 1997.
[53] Cos, I J, Miller, M L, Minka, T P, Papathornas, T V, Yianilos, P N. The Bayesian Image Retrieval System, PicHunter: Theory, Implementation, and Psychophysical Experiments. IEEE Tran. On Image Processing, Vol 9, Issue I, Jan,2000, pp. 20-37.
[54] 尹荣，郑兆瑞，郭海儒．一种基于图元的多级图像检索系统．太原理工大学学报，2004年第3期
[55] http://icl.pku.edu.cn/navigation/
[56] Orlega M, Rui Y, Chankrabarli K. Supporting similarity queries in MARS. ACM Conference on Multimedia, 1997.
[57] Cosman P C, Oehler K L. Using vector quantization for image processing. Proc of IEEE, 1993, 81(9): 1326～1341.
[2] M Ioka. A method of defining the similarity of image on the basis of color information. Technical Report RT-0030, IBM Research, November 1989.
[3] M J Swain and D H Ballard. Color indexing. International Journal of Computer Vision, Vol. 7, No. 1, pp. 11-32, 1991.
[4] Y Gong, H J Zhang and T C Chua. An image database system with content capturing and fast image indexing abilities. Proc. IEEE International Conference on Multimedia Computing and Systems, Boston, 14-19 May 1994, pp. 121-130.
[5] M Stricker and M Orengo. Similarity of color image, SPIE Storage and Retrieval for Image and Video Database Ⅲ, vol. 2185, pp. 381-392, Feb. 1995.
[6] G Pass and R Zabih. Histogram refinement for content-based image retrieval. IEEE Workshop on Applications of Computer Vision, pp. 96-102, 1996.
[7] W Y Ma and B S Manjunath. Edge flow: a framework of boundary detection and image segmentation. IEEE Int. Conf on Computer Vision and Pattern Recognition. pp.744-749, Puerto Rico, June 1997.
[8] John R Smith and Shih-Fu Chang. Automated binary texture feature sets for image retrieval. In Proc. IEEE Int. Conf. Acoust, Speech, and Signal Proc., May 1996.
[9] Robert M Haralick, K Shanmugam, and Its'hak Dinstein. Textnre features for image classification. IEEE Trans. On Sys, Man, and Cyb, SMC-3(6):610-621, 1973.
[10] H Tamura, S Mori and T Yamawaki. Texture features corresponding to visual perception. IEEE Trans. On Systems, Man, and Cybernetics, vol. Smc-8, no.6, June 1978.
[11] Niblack, et al. The QBIC project: querying images by content using color, texture, and shape. Proc. Of SPIE, Storage and Retrieval for Image and Video Databases, Vol. 1908, February 1993, San Jose. pp. 173-187.
[12] 郑南宁．计算机视觉与模式识别．国防工业出版社，1998年．
[13] J Mao and A K Jain. Texture classification and segmentation using muitiresolution simultaneous autoregressive models. Pattern Recognition, Vol. 25, No. 2, pp. 173-188, 1992.
[14] R W Picard, T Kabir and F Liu. Real-time recognition with the entire Brodatz texture database. Proc. IEEE Int, Conf. on Computer Vision and Pattern Recognition, pp. 638-639, New York, June 1993.
[15] B S Manjunath and W Y Ma. Texture features for browsing and retrieval of image
data. IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 18, No.8, pp.837-842, Aug. 1996.
[16] T Chang and C C Jay Kuo. Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. On Image Processing, vol. 2, no. 4, pp. 429-441, October 1993.
[17] A Laine and J Fan. Texture classification by wavelet packet signatures. IEEE Trans. Pattern Analysis and Machin Intelligence, vol. 15. 11, pp. 1186-1191, Nov. 1993.
[18] W Y Ma and B S Manjunath. A comparison of wavelet features for texture annotation. Proc. of IEEE Int. Conf. on Image Processing, vol. Ⅱ, pp. 256-259, Washington D. C., Oct. 1995.
[19] M K Hu. Visual pattern recognition by moment invariants, in J. K. Aggarwal, R. O.Duda, and A. Rosenfeld, Computer Methods in Image analysis, IEEE computer Society, Los Angeles, CA, 1977.
[20] A K Jain. Fundamentals of Digital Image Processing. Englewood Cliffs, Prentice Hall, 1989.
[21] L Yang and F Algregtsen. Fast computation of invariant geometric moments: A new method giving correct results. Proc. IEEE Int. Conf. On Image Processing, 1994.
[22] Deepak Kapur, Y N Lakshman and Tushar Saxena. Computing invariants using elimination methods. In Proc. IEEE Int. Conf. on Image roc, 1995.
[23] David Copper and Zhibin Lei. On representation and invariant recognition of complex objects based on patches and parts. In Spinger Lecture Notes in Computer Science series, 3D Object Representation for Computer Vision. M. Hebert, J. Ponce, T. Boult, A. Gross, Eds., New York,: Springer, 1995, pp. 139-153.
[24] S-K Chang, Q Y Shi and C Y Yan. Iconic indexing by 2-D strings. IEEE Trans. Pattern Anal. Machine Intell., 9(3),pp. 413-428, May 1987.
[25] V H Gudivada and V V Raghavan. Design and evaluation of algorithms for image retrieval by spatial similarity. ACM Trans. On Information Systems, vol. 13, no. 2.pp. 115-144, April 1995.
[26] M Lybanon, S Lea and S Himes. Segmentation of diverse image types using opening and closing, In Proc. IEEE Int. Conf. on Image Proc. 1994.
[27] X Q Li, Z W Zhao, H D Cheng, C M Huang and R W Harris. A fuzzy logic approach to image segmentation. In Proc. IEEE Int. Conf. on Image Proc. 1994.
[28] Ramin Samadani and Cecilia Han. Computer-assisted extraction of boundaries from image. In Proc. SPIE Storage and Retrieval for Image and Video Databases, 1993.
[29] Dirk Daneels, D Campenhout, Wayne Niblack, Will Equitz, Ron Barber, Erwin Bellon, and Freddy Fierens. Interactive outlining: An improved approach using active contours. In Proc. SPIE Storage and Retrieval for Image and Video Databases, 1993.
[30] Yong Rui, Alfred C She and Thomas S Huang. Automated shape segmentation using
attraction-based grouping in spatial-color-texture space. In Proc. IEEE Int. Conf. on Image Proc., 1996.
[31] A Celentano, V D Lecce. A FFT based technique for image signature generation[A]. Proc SPIE: Storage and Retrieval for Image and Video Databases V 3022[C]. Newport Beach CA USA: 1997.457-466.
[32] R Brunelli, O Mich. Image retrieval by examples[J]. IEEE Transaction on Multimedia, 2000, 2(3): 164-171.
[33] 魏海，沈兰荪．小波变换域内基于方向梯度相角直方图的图像检索算法[J]．电路与系统学报，2001，6(2)：20-24．
[34] R Reevesm, K Kubik, W Osberger. Texture characterization of compressed aerial images using DCT coefficients[A]. Proc. SPIE: Storage and Retrieval for Image and Video Databases V3022[C]. Newport Beach CA USA: 1997, 398-407.
[35] M Shneier, M S Mottaleb. Exploiting the JPEG compression scheme for image retrieval[J]. IEEE Trans, Pattern Analysis and Machine intelligence, 1996, 18(8): 849-853.
[36] S. K. Chang, Iconic indexing by 2D string[J]. IEEE Trans pattern analysis and machine intelligence, 1984, 6(4): 413-428.
[37] T Chang, C C Kuo. Texture analysis and classification with tree-structured wavelet transform[J]. IEEE Trans Image Processing, 1993, 2(4): 429-441.
[38] M K Mandal, T Aboulnasr, S Panchanathan. Image indexing using moments and wavelets[J]. IEEE Trans Consumer Electronics, 1996, 42(3): 557-565.
[39] J L Chen, A Kundu. Rotation and gray scale invariant texture identification using wavelet decomposition and hidden Markov model[J]. IEEE Trans Pattern Analysis and Machine Intelligence, 1994, 16(2): 208-214.
[40] M K Mandal, F Idris, S Panchanathan. A critical evaluation of image and video indexing techniques in the compressed domain[J]. Image and Vision Computing, 1999, 17:513-529.
[41] F Idris, S Panchanathan. Storage and retrieval of compressed images[J]. IEEE Trans Consumer Electronics, 1995, 41: 937-941.
[42] T Ida, Y Sambonsugi. Image segmentation using fractal coding[J]. IEEE Trans Circus and Systems for Video Technology, 1995, 5: 567-570.
[43] F Idris, S Panchanathan. Image indexing using wavelet vector quantization[A]. Digital Linage Storage Archiving Systems[C]. Philadelpia Pa USA: SPIE Proceedings, 1995.
[44] H J Zhang and D Zhong. A Scheme for visual feature-based image indexing. Proc.of SPIE conf. on Storage and Retrieval for Image and Video Databases Ⅲ,pp.36-46,Sam Jose, Feb. 1995.
[45] Fred Attneave. Dimensions of Similarity. American Journal of Psychology,
63:516-556,1950.
[46] Tversky, Amos(1977). Features of Similarity. Psychological Review, 84-4,pp.327-352.
[47] 樊玮虹，薛峰等．一种用于图像检索的聚类方法．计算机工程，200，26(2)：28～29．
[48] 伯晓晨，刘建平．基于颜色直方图的图像检索．中国图像图形学报．1999，4(1)：33～37．
[49] D White and R Jain. Similarity indexing: Algorithms and Performance. Proc. SPIE Storage and Retrieval for Image and Video Database, 1996.
[50] R Ng and A Sedighian. Evaluating Multi-dimensional Indexing Structures for Images Transformed by Principal Component Analysis. Proc. SPIE Storage and Retrieval for Image and Video Database, 1996.
[51] Rui Y, Huang T S, Ortega M. Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Trans on Circuits and Video Technology, 1998, 8(5)
[52] Rui, Y, Huang, T S, Mehrotra, S. Content-based Image Retrieval with Relevance Feedback in MARS. in Proc. IEEE Int. Conf. On Image Processing. 1997.
[53] Cos, I J, Miller, M L, Minka, T P, Papathornas, T V, Yianilos, P N. The Bayesian Image Retrieval System, PicHunter: Theory, Implementation, and Psychophysical Experiments. IEEE Tran. On Image Processing, Vol 9, Issue I, Jan,2000, pp. 20-37.
[54] 尹荣，郑兆瑞，郭海儒．一种基于图元的多级图像检索系统．太原理工大学学报，2004年第3期
[55] http://icl.pku.edu.cn/navigation/
[56] Orlega M, Rui Y, Chankrabarli K. Supporting similarity queries in MARS. ACM Conference on Multimedia, 1997.
[57] Cosman P C, Oehler K L. Using vector quantization for image processing. Proc of IEEE, 1993, 81(9): 1326～1341.
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