Region-based image retrieval with high-level semantics using decision tree learning

Pattern Recognition

Volumn 41, Issue 8, 2008, Pages 2554-2570

  Liu, Ying ^a   Zhang, Dengsheng ^a   Lu, Guojun ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

CBIR; Decision tree learning; RBIR; Semantic image retrieval; Semantic template

Indexed keywords

ALGORITHMS; DECISION TREES; LEARNING SYSTEMS; PROBLEM SOLVING; SEMANTICS;

LOW-LEVEL FEATURES; SEMANTIC IMAGE RETRIEVAL; SYSTEM SEGMENTS;

IMAGE RETRIEVAL;

EID: 42749095102     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2007.12.003     Document Type: Article

References (43)

1. 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
2. Pentland A., Picard R.W., and Scaroff S. Photobook: content-based manipulation for image databases. Int. J. Comput. Vision 18 3 (1996) 233-254
3. Gupta A., and Jain R. Visual information retrieval. Commun. ACM 40 5 (1997) 70-79
4. J.R. Smith, S.F. Chang, Visualseek: a fully automatic content-based query system, in: Proceedings of ACM International Conference on Multimedia, 1996, pp. 87-98.
5. W.Y. Ma, B. Manjunath, Netra: a toolbox for navigating large image databases, in: Proceedings of International Conference on Image Processing, 1997, pp. 568-571.
6. Wang J.Z., Li J., and Wiederhold G. Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Mach. Intell. 23 9 (2001) 947-963
7. F. Jing, M. Li, L. Zhang, H. Zhang, B. Zhang, Learning in region-based image retrieval, in: Proceedings of International Conference on Image and Video Retrieval (CIVR2003), 2003, pp. 206-215.
8. Smeulders A.W.M., Worring M., Santini S., 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
9. X.S. Zhou, T.S. Huang, CBIR: from low-level features to high-level semantics, in: Proceedings of SPIE Image and Video Communication and Processing, San Jose, CA, January 2000, pp. 426-431.
10. Y. Liu, D.S. Zhang, G. Lu, W.-Y. Ma, A survey of content-based image retrieval with high-level semantics, Pattern Recognition, 40 (1) (2007) 262-282.
11. C.P. Town, D. Sinclair, Content-based image retrieval using semantic visual categories, Society for Manufacturing Engineers, Technical Report MV01-211, 2001.
12. E. Chang, S. Tong, SVM active-support vector machine active learning for image retrieval, in: Proceedings of ACM International Multimedia Conference, October 2001, pp. 107-118.
13. L. Fei-Fei, R. Fergus, P. Perona, Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories, in: Proceedings of Computer Vision and Pattern Recognition, Workshop on Generative-Model Based Vision, 2004, pp. 178-185.
14. L. Cao, Li. Fei-Fei, Spatially coherent latent topic model for concurrent object segmentation and classification, in: Proceedings of IEEE International Conference in Computer Vision (ICCV), 2007.
15. Nguyen G.P., and Worring M. Relevance feedback based saliency adaptation in CBIR. ACM Multimedia Syst. 10 6 (2005) 499-512
16. V. Mezaris, I. Kompatsiaris, M.G. Strintzis, An ontology approach to object-based image retrieval, in: Proceedings of International Conference on Image Processing, 2003, pp. 511-514.
17. Tao D., Tang X., Li X., and Wu X. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans. Pattern Anal. and Mach. Intel. (TPAMI) 28 7 (2006) 1088-1099
18. Tao D., Tang X., Li X., and Rui Y. Kernel direct biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm. IEEE Trans. Multimedia (TMM) 8 4 (2006) 716-727
19. D. Tao, X. Li, S.J. Maybank, Negative samples analysis in relevance feedback, IEEE Trans. Knowl. Data Eng. (TKDE), 19 (4) (2007), 568-580.
20. Schreiber G., Dubbeldam B., Wielemaker J., and Wielinga B. Ontology-based photo annotation. IEEE Intell. Syst. 16 3 (2001) 66-74
21. D. Cai, X. He, Z. Li, W.-Y. Ma, J.-R. Wen, Hierarchical clustering of WWW image search results using visual, textual and link information, in: Proceedings of ACM International Conference on Multimedia, 2004, pp. 952-959.
22. Y. Zhuang, X. Liu, Y. Pan, Apply semantic template to support content-based image retrieval, SPIE, Storage and Retrieval for Media Databases, vol. 3972, 2000, pp. 442-449.
23. H. Feng, R. Shi, T.-S. Chua. A bootstrapping framework for annotating and retrieving WWW images, in: Proceedings of ACM International Conference on Multimedia, 2004, pp. 960-967.
24. J.R. Smith, C.S. Li, Decoding image semantics using composite region templates, in: Proceedings of Workshop on Content-Based Access of Image and Video Libraries (CBAIVL-98), June 1998, pp. 9-13.
25. S.-F. Chang, W. Chen, H. Sundaram, Semantic visual templates: linking visual features to semantics, in: Proceedings of International Conference on Image Processing (ICIP), Workshop on Content Based Video Search and Retrieval, October 1998, pp. 531-534.
26. Quinlan J.R. Induction of decision trees. Mach. Learning 1 (1986) 81-106
27. M. Pal, P.M. Mather, Decision tree based classification of remotely sensed data, in: Proceedings of 22nd Asian Conference on Remote Sensing (ACRS), Singapore, November 2001, pp. 245-248.
28. J.R. Quinlan, C4.5, Program for Machine Learning, Morgan Kaufmann, Los Altos, CA, 1993.
29. Perner P., and Trautzsch S. Multi-interval discretization methods for decision tree learning. In: Amin A., Dori D., Pudil P., and Freeman H. (Eds). Advances in Pattern Recognition (1998), Springer, Berlin 475-482
30. Breslow L.A., and Aha D.W. Simplifying decision trees: a survey. Knowl. Eng. Rev. 12 1 (1997) 1-40
31. Breiman L., Friedman J.H., Olshen R.A., and Stone C.J. Classification and Regression Trees (1984), Chapman & Hall, New York
32. Mitchell T. Decision tree learning. In: Mitchell T. (Ed). Machine Learning (1997), The McGraw-Hill Companies, Inc., New York 52-78
33. I.K. Sethi, I.L. Coman, Mining association rules between low-level image features and high-level concepts, SPIE Data Mining and Knowledge Discovery, vol. 3, 2001, pp. 279-290.
34. U.M. Fayyad, K.B. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, in: Proceedings of 13th International Joint Conference on Artificial Intelligence (IJCAI), France, 1993, pp. 1022-1027.
35. J. Dougherty, R. Kohavi, M. Sahami, Supervised and unsupervised discretization of continous features, in: Proceedings of 12th International Conference on Machine Learning 1995, pp. 194-202.
36. Y. Rubner, C. Tomasi, L.J. Guibas, A metric for distributions with applications to image databases, in: Proceedings of IEEE International Conference on Computer Vision (ICCV'98), January 1998, pp. 59-67.
37. 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
38. Y. Liu, D.S. Zhang, G. Lu, W.-Y. Ma, Region-based image retrieval with perceptual colors, in: Proceedings of Pacific-Rim Multimedia Conference (PCM). December 2004, pp. 931-938.
39. D.S. Zhang, G. Lu, Content-based image retrieval using gabor texture features, in: Proceedings of First IEEE Pacific-Rim Conference on Multimedia (PCM'00), Sydney, Australia, December 13-15, 2000, pp. 392-395.
40. Y. Liu, D.S. Zhang, G. Lu, W.-Y. Ma, Study on texture feature extraction in region-based image retrieval system, in: Proceedings of International Multimedia Modelling Conference (MMM2006), Beijing, 2006. pp. 264-271.
41. 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
42. 〈http://www.cs.waikato.ac.nz/ml/weka/〉.
43. 〈http://www.iti.gr/SCHEMA/〉.