A content-based image retrieval scheme allowing for robust automatic personalization

Proceedings of the 6th ACM International Conference on Image and Video Retrieval, CIVR 2007

Volumn , Issue , 2007, Pages 1-8

  Chatzis, Sotirios ^a   Doulamis, Anastasios ^a   Varvarigou, Theodora ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

Mixture models; Personalization; T distributions

Indexed keywords

FEEDBACK; ROBUST CONTROL; SEMANTICS; SENSORY PERCEPTION; USER INTERFACES;

CONTENT BASED IMAGE RETRIEVAL SYSTEMS; HUMAN PERCEPTION; MIXTURE MODELS; RELEVANCE FEEDBACK; ROBUST AUTOMATIC PERSONALIZATION;

CONTENT BASED RETRIEVAL;

EID: 36849048187     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1282280.1282281     Document Type: Conference Paper

References (21)

1. MPEG-7 Visual Part of experimentation Model Version 2.0. ISO/MPEG MPEG-7 Output Document, 1999.
2. Y. Avrithis, N. Doulamis, A. Doulamis, and S. Kollias. Optimization methods for key-frames and scenes extraction. Comput. Vis. Image Understanding, 75(1/2):3-24, Jul/Aug. 1999.
3. N. Babaguchi, K. Ohara, and T. Ogura. Effect of personalization on retrieval and summarization of sports video. In Joint Conference of the Fourth International Conference on Information, Communications and Signal Processing, and the Fourth Pacific Rim Conference on Multimedia, volume 2, pages 940-944, 2003.
4. Y. Chen, X. S. Zhou, and T. Huang. One-class SVM for learning in image retrieval. In Proc. IEEE Int 'l Conf. Image Processing, 2001, volume 1, pages 34-37, 2001.
5. I. Cox, M. L. Miller, S. M. Omohundro, and P. N. Yianilos. Pichunter: Bayesian relevance feedback for image retrieval. In Proc. Int. Conf. Pattern Recognition, 1996, volume 3, pages 362-369, 1996.
6. A. Doulamis and N. Doulamis. Generalized nonlinear relevance feedback for interactive content-based retrieval and organization. IEEE Trans. Circuits Syst. Video Technol., 14(5):656-671, 2004.
7. T. S. Huang and X. S. Zhou. Image retrieval by relevance feedback: from heuristic weight adjustment to optimal learning methods. In Proc. IEEE Int'l Conf. Image Processing, 2001, volume 3, pages 2-5, 2001.
8. Y. Ishikawa, R. Subramanya, and C. Faloutsos. Mindreader: Query databases through multiple examples. In Proc. 24th VLDB Conf. pages 218-227, 1998.
9. C. J. Leggetter and P. C. Woodland. Maximum likelihood linear regression for speaker adaptation of continuous density hidden markov models. Computer Speech & Language, 9(2):171-185, 1995.
10. B. Manjunath, P. Wu, S. Newsam, and H. Shin. A texture descriptor for browsing and similarity retrieval. J. Signal Processing: Image Comm., 16:33-42, 2000.
11. P. Muneesawang and L. Guan. Image retrieval with embeded sub-class information using Gaussian mixture models. In Proc. Int. Conference Multimedia and Expo, 2003, volume 1, pages 769-772, 2003.
12. D. Peel and G. J. McLachlan. Robust mixture modeling using the t distribution. Statistics and Computing, 10(4):339-348, 2000.
13. Y. Rui, T. Huang, and S. Mehrotra. Content-based image retrieval with relevance feedback in MARS. In Proc. IEEE Int'l Conf. Image Processing, 1997, volume 2, pages 815-818, 1997.
14. Y. Rui, T. Huang, M. Ortega, and S. Mehrotra. Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Trans. Circuits Syst. Video Technol., 8:644-655, 1998.
15. G. Salton and M. J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, New York, 1982.
16. S. Shoham. Robust clustering by deterministic agglomeration EM of mixtures of multivariate t distributions. Statistics and Computing, 35(55):1127-1142, 2002.
17. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Analysis and Machine Intelligence, 22(12):1349-1380, 2000.
18. Z. Su, H. Zhang, S. Li, and S. Ma. Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning. IEEE Trans. Image Processing, 12(8):924-937, 2003.
19. N. Vasconcelos and A. Lippman. Learning from user feedback in image retrieval systems. In Proceedings of Neural Information Processing Systems 12, volume 1, 1999.
20. N. Vasconcelos and A. Lippman. Statistical models of video structure for content analysis and characterization. IEEE Trans. Image Processing, 9:3-19, 2000.
21. X. S. Zhou and T. Huang. Small sample learning during multimedia retrieval using biasmap. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2001, volume 1, pages 11-17, 2001.