Quantization-based probabilistic feature modeling for kernel design in content-based image retrieval

Proceedings of the ACM International Multimedia Conference and Exhibition

Volumn , Issue , 2006, Pages 23-32

  Xie, Hua ^a   Andreu, Victor ^b   Ortega, Antonio ^b  

^a CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^b University of Southern California ^*  (United States)

Author keywords

Kernel; Probabilistic modeling; Quantization; Relevance feedback; Support vector machines

Indexed keywords

PROBABILISTIC MODELING; PROBABILISTIC QUANTITIES; RELEVANCE FEEDBACK;

FEATURE EXTRACTION; FUNCTION EVALUATION; MAXIMUM LIKELIHOOD ESTIMATION; PROBABILISTIC LOGICS; SUPPORT VECTOR MACHINES; VECTOR QUANTIZATION;

CONTENT BASED RETRIEVAL;

EID: 34547489435     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1178677.1178684     Document Type: Conference Paper

References (23)

1. Corel stock photo library. Corel Corp. Ontario, Canada.
2. J. A. Bilmes. A gentle tutorial on the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. Technical Report, University of Berkeley, ICSI-TR-97-021, 1997.
3. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge discovery, pages 121-167, 1998.
4. E. Chang and S. Tong. Support vector machine active learning for image retrieval. ACM International Conference on Multimedia, pages 107-118, October 2001.
5. J.-Y. Chen, C. A. Bouman, and J. Dalton. Similarity pyramids for browsing and organization of large image databases. Proc. of SPIE Conf. on Human Vision and Electronic Imaging III, 3299, Jan. 1998.
6. Y. Chen, X. Zhou, and T. S. Huang. One-class support vector machine for learning in image retrieval. IEEE International Conference on Image Processing, 2001.
7. T. M. Cover and J. A. Thomas. Elements of Information Theory. John Wiley & Sons, INC, 1991.
8. R. O. Duda and P. E. Hart. Pattern classification and scene analysis. Wiley-interscience Publication, 1973.
9. Gray and Olshen. Vector quantization and density estimation. In SEQS: Sequences '91, 1998.
10. C. Hoi, C. Chan, K. Huang, M. R. Lyu, and I. King. Biased support vector machine for relevance feedback in image retrieval. International joint conference on Neural Networks, 2004.
11. P. Hong, Q. Tian, and T. S. Huang. Incorporate support vector machines to content-based image retrieval with relevance feedback. IEEE International Conference on Image Processing, 2000.
12. T. S. Huang and X. S. Zhou. Image retrieval with relevance feedback: From heuristic weight adjustment to optimal learning methods. IEEE International Conference on Image Processing, 2001.
13. T. Jaakkola, M. Diekhans, and D. Haussler. Using the fisher kernel method to detect remote protein homologies. Proc. of the International Conference on Intelligent Systems for Molecular Biology, Aug. 1999.
14. T. S. Jaakkola and D. Haussler. Exploiting generative models in discriminative classifiers. In Proceedings of the 1998 conference on Advances in neural information processing systems II. pages 487-493, Cambridge, MA, USA, 1999. MIT Press.
15. T. Joachims. Advances in Kernel Methods - Support Vector Learning, chapter 11: Making Large-scale support vector machine Learning Practical. MIT Press, 1998.
16. P. J. Moreno, P. Ho, and N. Vasconceles. A Kullback-Leibler divergence based kernel for SVM classification in multimedia applications. Neural Information Processing Systems, 2003.
17. J. Peng, B. Bhanu, and S. Qing. Probabilistic feature relevance learning for content-based image retrieval. Computer Vision and Image understanding, 75:150-164, 1999.
18. B. Picinbono and P. Duvaut. Optimum quantization for detection. IEEE Trans on Communications, 36:1254-1258, 1988.
19. J. Rissanen, T. P. Speed, and B. Yu. Density estimation by stochastic complexity. IEEE Trans, on Information Theory, pages 315-323, March 1992.
20. Y. Rui, T. S. Huang, M. Ortega, and S. Mehrotra. Relevance feedback: A power tool in interactive content-based image retrieval. IEEE trans on Circuits and Systems for Video Technology, 8:644-655, 1998.
21. N. Vasconcelos. Exploiting group structure to improve retrieval accuracy and speed in image databases. In International conference on Image Processing, 2002.
22. Q. Wang, S. R. Kullkarni, and S. Verdu. Divergence estimation of continuous distributions based on data-dependent partitions. IEEE Trans on Information Theory, 51, 2005.
23. H. Xie and A. Ortega. User preference information based kernel for svm active learning in content-based image retrieval. 6th ACM SIGMM International Workshop on Multimedia, Information Retrieval, 2004.