A survey of content-based image retrieval with high-level semantics

Pattern Recognition

Volumn 40, Issue 1, 2007, Pages 262-282

  Liu, Ying ^a   Zhang, Dengsheng ^a   Lu, Guojun ^a   Ma, Wei Ying ^b  

^a MONASH UNIVERSITY   (Australia)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

Content based image retrieval; High level semantics; Semantic gap; Survey

Indexed keywords

ALGORITHMS; HTML; LEARNING SYSTEMS; SEMANTICS; SURVEYS; USER INTERFACES;

CONTENT-BASED IMAGE RETRIEVAL; HIGH-LEVEL SEMANTICS; SEMANTIC GAP;

IMAGE RETRIEVAL;

EID: 33749264994     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2006.04.045     Document Type: Article

References (139)

1. J. Eakins, M. Graham, Content-based image retrieval, Technical Report, University of Northumbria at Newcastle, 1999.
2. I.K. Sethi, I.L. Coman, Mining association rules between low-level image features and high-level concepts, Proceedings of the SPIE Data Mining and Knowledge Discovery, vol. III, 2001, pp. 279-290.
3. Chang S.K., and Liu S.H. Picture indexing and abstraction techniques for pictorial databases. IEEE Trans. Pattern Anal. Mach. Intell. 6 4 (1984) 475-483
4. Faloutsos C., Barber R., Flickner M., Hafner J., Niblack W., Petkovic D., and Equitz W. Efficient and effective querying by image content. J. Intell. Inf. Syst. 3 3-4 (1994) 231-262
5. Pentland A., Picard R.W., and Scaroff S. Photobook: content-based manipulation for image databases. Int. J. Comput. Vision 18 3 (1996) 233-254
6. Gupta A., and Jain R. Visual information retrieval. Commun. ACM 40 5 (1997) 70-79
7. J.R. Smith, S.F. Chang, VisualSeek: a fully automatic content-based query system, Proceedings of the Fourth ACM International Conference on Multimedia, 1996, pp. 87-98.
8. W.Y. Ma, B. Manjunath, Netra: a toolbox for navigating large image databases, Proceedings of the IEEE International Conference on Image Processing, 1997, pp. 568-571.
9. Wang J.Z., Li J., and Wiederhold G. SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23 9 (2001) 947-963
10. Long F., Zhang H.J., and Feng D.D. Fundamentals of content-based image retrieval. In: Feng D. (Ed). Multimedia Information Retrieval and Management (2003), Springer, Berlin
11. Rui Y., Huang T.S., and Chang S.-F. Image retrieval: current techniques, promising directions, and open issues. J. Visual Commun. Image Representation 10 4 (1999) 39-62
12. A. Mojsilovic, B. Rogowitz, Capturing image semantics with low-level descriptors, Proceedings of the ICIP, September 2001, pp. 18-21.
13. X.S. Zhou, T.S. Huang, CBIR: from low-level features to high-level semantics, Proceedings of the SPIE, Image and Video Communication and Processing, San Jose, CA, vol. 3974, January 2000, pp. 426-431.
14. Y. Chen, J.Z. Wang, R.Krovetz, An unsupervised learning approach to content-based image retrieval, IEEE Proceedings of the International Symposium on Signal Processing and its Applications, July 2003, pp. 197-200.
15. Smeulders A.W.M., Worring M., Gupta A., and Jain R. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22 12 (2000) 1349-1380
16. F. Jing, M. Li, L. Zhang, H.-J. Zhang, B. Zhang, Learning in region-based image retrieval, Proceedings of the International Conference on Image and Video Retrieval (CIVR2003), 2003, pp. 206-215.
17. H. Feng, D.A. Castanon, W.C. Karl, A curve evolution approach for image segmentation using adaptive flows, Proceedings of the International Conference on Computer Vision (ICCV'01), 2001, pp. 494-499.
18. W.Y. Ma, B.S. Majunath, Edge flow: a framework of boundary detection and image segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1997, pp. 744-749.
19. Shi J., and Malik J. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 22 8 (2000) 888-905
20. D. Comaniciu, P. Meer, Robust analysis of feature spaces: color image segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1997, pp. 750-755.
21. Stanchev P.L., Green Jr. D., and Dimitrov B. High level color similarity retrieval. Int. J. Inf. Theories Appl. 10 3 (2003) 363-369
22. K.A. Hua, K. Vu, J.-H. Oh, SamMatch: a flexible and efficient sampling-based image retrieval technique for large image databases, Proceedings of the Seventh ACM International Multimedia Conference (ACM Multimedia'99), November 1999, pp. 225-234.
23. Deng Y., and Manjunath B.S. Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Anal. Mach. Learn. (PAMI) 23 8 (2001) 800-810
24. H. Feng, T.-S. Chua, A boostrapping approach to annotating large image collection, Workshop on Multimedia Information Retrieval in ACM Multimedia, November 2003, pp. 55-62.
25. Y. Liu, D.S. Zhang, G. Lu, W.-Y. Ma, Region-based image retrieval with perceptual colors, Proceedings of the Pacific-Rim Multimedia Conference (PCM), December 2004, pp. 931-938.
26. Carson C., Belongie S., Greenspan H., and Malik J. Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE Trans. Pattern Anal. Mach. Intell. 8 8 (2002) 1026-1038
27. R. Shi, H. Feng, T.-S. Chua, C.-H. Lee, An adaptive image content representation and segmentation approach to automatic image annotation, International Conference on Image and Video Retrieval (CIVR), 2004, pp. 545-554.
28. C.P. Town, D. Sinclair, Content-based image retrieval using semantic visual categories, Society for Manufacturing Engineers, Technical Report MV01-211, 2001.
29. J.R. Smith, C.-S. Li, Decoding image semantics using composite region templates, IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL-98), June 1998, pp. 9-13.
30. Leow W.K., and Lai S.Y. Scale and orientation-invariant texture matching for image retrieval. In: Pietikainen M.K. (Ed). Texture Analysis in Machine Vision (2000), World Scientific, Singapore
31. V. Mezaris, I. Kompatsiaris, M.G. Strintzis, An ontology approach to object-based image retrieval, Proceedings of the ICIP, vol. II, 2003, pp. 511-514.
32. Plataniotis K.N., and Venetsanopoulos A.N. Color Image Processing and Applications (2000), Springer, Berlin
33. Manjunath B.S., et al. Color and texture descriptors. IEEE Trans. CSVT 11 6 (2001) 703-715
34. J.Z. Wang, J. Li, D. Chan, G. Wiederhold, Semantics-sensitive retrieval for digital picture libraries, Digital Library Magazine, vol. 5(11), 1999.
35. active-support vector machine active learning for image retrieval, Proceedings of the ACM International Multimedia Conference, October 2001, pp. 107-118.
36. X. Zheng, D. Cai, X. He, W.-Y. Ma, X. Lin, Locality preserving clustering for image database, Proceedings of the 12th ACM Multimedia, October 2004.
37. Manjunath B.S., et al. Introduction to MPEG-7 (2002), Wiley, New York
38. Gevers T., and Smeulders A. Content-based image retrieval by viewpoint-invariant color indexing. Image Vision Comput. 17 (1999) 475-488
39. W. Wang, Y. Song, A. Zhang, Semantics retrieval by content and context of image regions, Proceedings of the 15th International Conference on Vision Interface (VI'2002), May 2002, pp. 17-24.
40. Plataniotis K.N., et al. Adaptive fuzzy systems for multichannel signal processing. Proc. IEEE 87 9 (1999) 1601-1622
41. R. Lukac, et al., Vector filtering for color imaging, IEEE Signal Process. Mag. (2005) 74-86.
42. P. Stanchev, Using image mining for image retrieval, IASTED Conference "Computer Science and Technology," Cancun, Mexico, May 2003, pp. 214-218.
43. Tamura H., Mori S., and Yamawaki T. Texture features corresponding to visual perception. IEEE Trans. Syst. Man Cybern. 8 6 (1978) 460-473
44. Liu F., and Picard R.W. Periodicity, directionality, and randomness: wold features for image modeling and retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 18 7 (1996) 722-733
45. Brodatz P. Textures, A Photographic Album for Artists & Designers (1966), Dover, New York, NY
46. Y. Liu, X. Zhou, W.Y. Ma, Extraction of texture features from arbitrary-shaped regions for image retrieval, International Conference on Multimedia and Expo (ICME04), Taipei, June 2004, pp. 1891-1894.
47. Huang P.W., and Dai S.K. Image retrieval by texture similarity. Pattern Recognition 36 (2003) 665-679
48. Mehrotra R., and Gary J.E. Similar-shape retrieval in shape data management. IEEE Comput. 28 9 (1995) 57-62
49. Mokhtarian F., and Abbasi S. Shape similarity retrieval under affine transforms. Pattern Recognition 35 (2002) 31-41
50. Song Y., Wang W., and Zhang A. Automatic annotation and retrieval of images. J. World Wide Web 6 2 (2003) 209-231
51. A. Mojsilovic, B. Rogowitz, ISee: perceptual features for image library navigation, Proceedings of the SPIE, Human Vision and Electronic Imaging, vol. 4662, 2002, pp. 266-277.
52. Chang S.K., Shi Q.Y., and Yan C.W. Iconic indexing by 2D string. IEEE Trans. Pattern Anal. Mach. Intell. 9 3 (1987) 413-428
53. W. Ren, M. Singh, C. Singh, Image retrieval using spatial context, Ninth International Workshop on Systems, Signals and Image Processing (IWSSIP'02), Manchester, November, 2002.
54. D. Androutsos, K.N. Plataniotis, A.N. Venetsanopoulos, Distance measures for color image retrieval, Proceedings of the International Conference on Image Processing, vol. 2, 1998, pp. 770-774.
55. Chen Z., and Zhu B. Some formal analysis of Rocchio's similarity-based relevance feedback algorithm. Inf. Retr. 5 (2002) 61-86
56. S. Ardizzoni, I. Bartolini, M. Patella, Windsurf: region-based image retrieval using wavelets, 10th International Workshop on Database and Expert Systems Applications, Florence, Italy, 1999, pp. 167-173.
57. Y. Rubner, C. Tomasi, L. Guibas, A metric for distributions with applications to image databases, Proceedings of the IEEE International Conference on Computer Vision (ICCV'98), January 1998, pp. 59-67.
58. B. Li, E. Chang, C.-T. Wu, DPF-a perceptual function for image retrieval, Proceedings of the International Conference on Image Processing (ICIP), vol. II, September 2002, pp. 597-600.
59. Berretti S., Bimbo A.D., and Pala P. Retrieval by shape similarity with perceptual distance and effective indexing. IEEE Trans. Multimedia 2 4 (2000) 225-239
60. Vasconcelos N., and Lippman A. A multiresolution manifold distance for invariant image similarity. IEEE Trans. Multimedia 7 1 (2005) 127-142
61. Kushki A., et al. Retrieval of image from artistic repositories using a decision fusion framework. IEEE Trans. Image Process. 13 3 (2004) 277-289
62. Kushki A., et al. Query feedback for interactive image retrieval. IEEE Trans. CSVT 14 5 (2004) 644-655
63. D. Cai, X. He, Z. Li, W.-Y. Ma, J.-R. Wen, Hierachical clustering of WWW image search results using visual, textual and link information, Proceedings of the ACM International Conference on Multimedia, 2004.
64. D. Cai, X. He, W.-Y. Ma, J.-R. Wen, H. Zhang, Organizing WWW images based on the analysis of page layout and web link structure, Proceedings of the International Conference on Multimedia and Expo (ICME), Taipei, 2004.
65. J. Ren, Y. Shen, L. Guo, A novel image retrieval based on representative colors, Proceedings of the Image and Vision Computing, N.Z., November 2003, pp. 102-107.
66. S. Kulkarni, B. Verma, Fuzzy logic for texture queries in CBIR, Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA), Xi'an, China, 2003, pp. 223-226.
67. C.-Y. Chiu, H.-C. Lin, S.-N. Yang, Texture retrieval with linguistic descriptors, IEEE Pacific Rim Conference on Multimedia, 2001, pp. 308-315.
68. Vailaya A., Figueiredo M.A.T., Jain A.K., and Zhang H.J. Image classification for content-based indexing. IEEE Trans. Image Process. 10 1 (2001) 117-130
69. L. Zhang, F. Liu, B. Zhang, Support vector machine learning for image retrieval, International Conference on Image Processing, October 2001, pp. 7-10.
70. Y. Zhuang, X. Liu, Y. Pan, Apply semantic template to support content-based image retrieval, Proceedings of the SPIE, Storage and Retrieval for Media Databases, vol. 3972, December 1999, pp. 442-449.
71. W. Chang, J. Wang, Metadata for multi-level content-based retrieval, Third IEEE Meta-Data Conference, April 1999.
72. H. Feng, R. Shi, T.-S. Chua, A bootstrapping framework for annotating and retrieving WWW images, Proceedings of the ACM International Conference on Multimedia, 2004.
73. M. Obeid, B. Jedynak, M. Daoudi, Image indexing and retrieval using intermediate features, Proceedings of the Ninth ACM International Conference on Multimedia, Ottawa, Canada, 2001, pp. 531-533.
74. D.M. Conway, An experimental comparison of three natural language color naming models, Proceedings of the East-West International Conference on Human-Computer Interactions, St. Petersburg, Russia, 1992, pp. 328-339.
75. Berk T., Brownston L., and Kaufman A. A new color-naming system for graphics language. IEEE Comput. Graphics Appl. 2 3 (1982) 37-44
76. A.R. Rao, G.L. Lohse, Towards a texture naming system: identifying relevant dimensions of texture, IEEE Proceedings of the Fourth Conference on Visualization, 1993, pp. 220-227.
77. J. Luo, A. Savakis, Indoor vs outdoor classification of consumer photographs using low-level and semantic features, International Conference on Image Processing (ICIP), vol II, October 2001, pp. 745-748.
78. Hastie T., Tibshirani R., and Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2001), Springer, New York
79. Vapnik V.N. Statistical Learning Theory (1998), Wiley, New York
80. W. Jin, R. Shi, T.-S. Chua, A semi-nai{dotless}̈ve bayesian method incorporating clustering with pair-wise constraints for auto image annotation, Proceedings of the ACM Multimedia, 2004.
81. S. Tong, E. Chang, Support vector machine active learning for image retrieval, Proceedings of the ACM International Conference on Multimedia, Ottawa, Canada, 2001, pp. 107-118.
82. N. Vasconcelos, A. Lippman, Library-based coding: a representation for efficient video compression and retrieval, Proceedings of the Data Compression Conference (DCC97), March 1997, pp. 121-130.
83. S.D. MacArthur, C.E. Brodley, C.-R. Shyu, Relevance feedback decision trees in content-based image retrieval, Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL'00), June 2000, pp. 68-72.
84. W.-C. Low, T.-S. Chua, Color-based relevance feedback for image retrieval, Proceedings of the International Workshop on Multimedia DBMS (IM-MMDBMS'98), August 1998, pp. 116-123.
85. M. Pal, P.M. Mather, Decision tree based classification of remotely sensed data, Proceedings of the 22nd Asian Conference on Remote Sensing (ACRS), Singapore, vol. 1, November 2001, pp. 245-248.
86. L.O. Hall, N. Chawla, K.W. Bowyer, Decision tree learning on very large data sets, IEEE International Conference on System, Man and Cybernetics (SMC) 1998, pp. 187-222.
87. J.R. Quanlan, Induction of decision tree, Machine Learning, vol. 1, Kluwer Acedemic Publisher, Boston, 1986, pp. 81-106.
88. U.M. Fayyad, K.B. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI), France, 1993, pp. 1022-1027.
89. Fayyad U.M., and Irani K.B. On the handling of continuous-valued attributes in decision tree generation. Mach. Learn. 8 (1992) 87-102
90. D. Stan, I.K. Sethi, Mapping low-level image features to semantic concepts, Proceedings of the SPIE: Storage and Retrieval for Media Databases, 2001, pp. 172-179.
91. M. Bilenko, S. Basu, R.J. Mooney, Integrating constraints and metric learning in semi-supervised clustering, Proceedings of the 21st International Conference on Machine Learning (ICML), July 2004, pp. 81-88.
92. A.Y. Ng, M.I. Jordan, Y. Weiss, On spectral clustering: analysis and an algorithm, Advances in Neural Information Processing Systems, vol. 14, MIT Press, Cambridge, MA, 2002.
93. Vasconcelos N. The design of end-to-end optimal image retrieval systems. Proceedings of the International Conference on ANN (2003), Istanbul, Turkey
94. Vasconcelos N. On the efficient evaluation of probabilistic similarity functions for image retrieval. IEEE Trans. Inf. Theory 50 7 (2004) 1482-1496
95. R. Fergus, P. Perona, A. Zisserman, Object class recognition by unsupervised scale-invariant learning, Proceedings of the Computer Vision and Pattern Recognition, 2003.
96. Y. Li, J. Bilmes, L.G. Shapiro, Object class recognition using images of abstract regions, International Conference on Pattern Recognition, August 2004.
97. Y. Li, L.G. Shapiro, J. Bilmes, A generative/discriminative learning algorithm for image classification, International Conference on Computer Vision, October 2005.
98. L. Fei-Fei, R. Fergus, P. Perona, Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories, Computer Vision and Pattern Recognition, Workshop on Generative-Model Based Vision, 2004.
99. Salton G. Automatic Text Processing (1989), Addison-Wesley, Reading, MA
100. Rui Y., Huang T.S., Ortega M., and Mehrotra S. Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans. Circuits Video Technol. 8 5 (1998) 644-655
101. Y. Rui, T.S. Huang, Optimizing learning in image retrieval, Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, June 2000, pp. 1236-1243.
102. Zhu X.S., and Huang T.S. Relevance feedback in image retrieval: a comprehensive review. Multimedia System 8 6 (2003) 536-544
103. Guo G.-D., Jain A.K., Ma W.-Y., and Zhang H.-J. Learning similarity measure for natural image retrieval with relevance feedback. IEEE Trans. Neural Networks 13 4 (2002) 811-820
104. Y. Rui, T.S. Huang, S. Mehrotra, Content-based image retrieval with relevance feedback in Mars, Proceedings of the IEEE International Conference on Image Processing, 1997, pp. 815-818.
105. Chen Z., and Zhu B. On the complexity of Rocchio's similarity-based relevance feedback algorithm (2005), ISAAC
106. Jing F., et al. Relevance feedback in region-based image retrieval. IEEE Trans. CSVT 14 5 (2004) 672-681
107. T.S. Huang, X.S. Zhou, M. Nakazato, Y. Wu, I. Cohen, Learning in content-based image retrieval, International Conference on Development and Learning (ICDL'02), 2002, pp. 155-162.
108. Q. Tian, Y. Yu, T.S. Huang, Incorporate discriminant analysis with EM algorithm in image retrieval, Proceedings of the International Conference on Multimedia and Expo (ICME), 2000, pp. 299-302.
109. Y. Lu, C. Hu, X. Zhu, H. Zhang, Q. Yang, A unified framework for semantics and feature based relevance feedback in image retrieval systems, ACM International Conference on Multimedia, 2000, pp. 31-37.
110. Doulamis A.D., and Doulamis N.D. Generalized nonlinear relevance feedback for iterative content-based retrieval and organization. IEEE Trans. CSVT 14 5 (2004) 656-671
111. S.-F. Chang, W. Chen, H. Sundaram, Semantic visual templates: linking visual features to semantics, International Conference on Image Processing (ICIP), Workshop on Content Based Video Search and Retrieval, vol. 3, October 1998, pp. 531-534.
112. Miller G., Beckwith R., Fellbaum C., Gross D., and Miller K. Introduction to Wordnet: an on-line lexical database. Int. J. Lexicography 3 (1990) 235-244
113. G. Qiu, K.-M. Lam, Spectrally layered color indexing, Proceedings of the International Conference on Image and Video Retrieval (CIVR), 2002, pp. 100-107.
114. S. Kim, S. Park, M. Kim, Central object extraction for object-based image retrieval, International Conference on Image and Video Retrieval (CIVR), 2003, pp. 39-49.
115. 〈http://www.corel.com〉.
116. Z. Yang, C.-C. Jay Kuo, Learning image similarities and categories from content analysis and relevance feedback, Proceedings of the ACM Multimedia Workshops, 2000, pp. 175-178.
117. X.Y. Jin, CBIR: difficulty, challenge, and opportunity, Microsoft PPT, October 2002.
118. H. Mueller, S. Marchand-Maillet, T. Pun, The truth about Corel-evaluation in image retrieval, Proceedings of the International Conference on Image and Video Retrieval (ICIVR), 2002, pp. 38-49.
119. N.V. Shirahatti, K. Barnard, Evaluating image retrieval, Proceedings of the Computer Vision and Pattern Recognition (CVPR), San Diego, CA, vol. 1, June 2005, pp. 955-961.
120. A. Mojsilovic, B. Rogowitz, Capturing image semantics with low-level descriptors, International Conference on Image Processing (ICIP), Greece, 2001, pp. 18-21.
121. J.A. Black, K. Kahol, P. Kuchi, G. Fahmy, S. Panchanathan, Characterizing the high-level content of natural images using lexical basis functions, Proceedings of the SPIE, vol. 5007, Human Vision and Electronic Imaging, Santa Clara, 2003, pp. 378-391.
122. J. Huang, S. Kuamr, M. Mitra, W.-J. Zhu, R. Zabih, Image indexing using color correlogram, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'97), 1997, pp. 762-765.
123. R. Zhao, W.I. Grosky, From feature to semantics: some preliminary results, Proceedings of the International Conference on Multimedia and Expo (ICME), 2000, pp. 679-682.
124. Zhao R., and Grosky W.I. Negotiating the semantic gap: from feature maps to semantic landscapes. J. Pattern Recognition 35 (2002) 593-600
125. Town C.P., and Sinclair D. Language-based querying of image collections on the basis of an extensible ontology. Int. J. Image Vision Comput. 22 3 (2004) 251-267
126. Lewis P.H., et al. An integrated content and metadata based retrieval system for art. IEEE Trans. Image Process. 13 3 (2004) 302-313
127. J.Z. Wang, Y. Du, Scalable integrated region-based image retrieval using IRM and statistical clustering, IEEE Proceedings of the ACM and IEEE Joint Conference on Digital Libraries, Roanoke, VA, ACM, June 2001, pp. 268-277.
128. Hartigan J.A., and Wong M.A. Algorithm AS136: a k-means clustering algorithm. Appl. Stat. 28 1 (1979) 100-108
129. Chahir Y., and Chen L. Spatialized multi-visual features-based image retrieval. Int. J. Comput. Appl. 6 4 (1999) 190-199
130. A. Guttman, R-tree: a dynamic index structure for spatial searching, Proceedings of the ACM SIGMOD International Conference on Management of Data, Boston, MA, 1984, pp. 47-57.
131. J. Robinson, The k-d-b-tree: a search structure for large multidimensional dynamic indexes, Proceedings of the ACM SIGMOD International Conference on Management of Data, 1981, pp. 10-18.
132. Finkel R., and Bentley J. Quad-tree: a data structure for retrieval on composite keys. Acta Inf. 4 1 (1974) 1-9
133. H. Yamamoto, H. Iwasa, N. Yokaya, H. Takemura, Content-based similarity retrieval of images based on spatial color distribution, 10th International Conference on Image Analysis and Processing, Venice, Italy, September 1999, pp. 951-956.
134. R. Weber, H.-J. Schek, S. Blott, A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces, Proceedings of the 24th VLDB Conference, New York, USA, 1998, pp. 194-205.
135. S. Berchtold, D.A. Keim, H.-P. Kriegel, The X-tree: an index structure for high-dimensional data, Proceedings of the 22nd VLDB Conference Mumbai (Bombay), India, 1996, pp. 28-39.
136. C. Yu, B.C. Ooi, K.-L. Tan, H.V. Jagadish, Indexing the distance: an efficient method to KNN processing, Proceedings of the 27th VLDB Conference, Roma, Italy, 2001, pp. 421-430.
137. A.P. Berman, L.G. Shapiro, Triangle-inequality-based pruning algorithms with triangle tries. Proceedings of the SPIE Conference on Storage and Retrieval for Image and Video Databases, January 1999.
138. I. Bartolini, P. Ciaccia, M. Patella, A sound algorithm for region-based image retrieval using an index, International Workshop on Database and Expert Systems Applications (DEXA), 2000, pp. 930-934.
139. Ogle V.E. CHABOT-retrieval from a relational database of images. Computer 28 9 (1995) 40-48