Active concept learning in image databases

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 35, Issue 3, 2005, Pages 450-466

  Dong, Anlei ^a   Bhanu, Bir ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Concept transduction; Mixture model; Model selection; Relevance feedback; Semi supervised expectation maximization (SS EM) algorithm

Indexed keywords

COMPUTATIONAL COMPLEXITY; CONTENT BASED RETRIEVAL; DATABASE SYSTEMS; LEARNING ALGORITHMS; MATHEMATICAL MODELS; PROBABILITY DENSITY FUNCTION;

CONCEPT TRANSDUCTION; MIXTURE MODEL; MODEL SELECTION; RELEVANCE FEEDBACK; SEMI SUPERVISED EXPECTATION-MAXIMIZATION (SS-EM) ALGORITHM;

LEARNING SYSTEMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER GRAPHICS; COMPUTER INTERFACE; DATA BASE; EVALUATION; FACTUAL DATABASE; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; INFORMATION RETRIEVAL; MATHEMATICAL COMPUTING; METHODOLOGY; SIGNAL PROCESSING; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER GRAPHICS; DATABASE MANAGEMENT SYSTEMS; DATABASES, FACTUAL; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; INFORMATION STORAGE AND RETRIEVAL; NUMERICAL ANALYSIS, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; SIGNAL PROCESSING, COMPUTER-ASSISTED; SUBTRACTION TECHNIQUE; USER-COMPUTER INTERFACE;

EID: 20444413355     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2005.846653     Document Type: Article

References (30)

1. A. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, "Content-based image retrieval at the end of the early years," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 12, pp. 1349-1380, Dec. 2000.
2. X. S. Zhou and T. S. Huang, "Relevance feedback for image retrieval: A comprehensive review," Multimedia Syst., vol. 8, no. 6, pp. 536-544, 2003.
3. J. J. Rocchio and G. Salton, "Information search optimization and iterative retrieval techniques," in Proc. American Federation of Information Processing Societies, vol. 27, 1965, pp. 293-305.
4. Y. Rui, T. S. Huang, M. Ortega, and S. Mehrotra, "Relevance feedback: A power tool for interactive content-based image retrieval," IEEE Trans. Circuits Syst. Video Technol., vol. 8, no. 5, pp. 644-655, Sep. 1998.
5. J. Peng, B. Bhanu, and S. Qing, "Probabilistic feature relevance learning for content-based image retrieval," Comput. Vis. Image Understand., vol. 75, no. 1-2, pp. 150-164, Jul.-Aug. 1999.
6. I. J. Cox, M. L. Matt, T. P. Minka, T. V. Papathoma, and P. N. Yianilos, "The Bayesian image retrieval system, PicHunter: Theory, implementation, and psychophysical experiments," IEEE Trans. Image Process., vol. 9, no. 1, pp. 20-37, Jan. 2000.
7. S. Tong and E. Chang, "Support vector machine active learning for image retrieval," in Proc. ACM Int. Conf. Multimedia, 2001, pp. 107-118.
8. P. Yin, B. Bhanu, K. Chang, and A. Dong, "Improving retrieval performance by long-term relevance information," in Proc. Int. Conf. Pattern Recognition, vol. III, Aug. 2002, pp. 533-536.
9. J. Fournier and M. Cord, "Long-term similarity learning in content-based image retrieval," in Proc. IEEE Int. Conf. Image Processing, vol. 1, Sep. 2002, pp. 441-444.
10. B. Bhanu and A. Dong, "Concept learning with fuzzy clustering and relevance feedback," Eng. Applicat. Artif. Intell., vol. 15, pp. 123-138, Apr. 2002.
11. C. T. Yu, W. S. Luk, and T. Y. Cheung, "A statistical model for relevance feedback in information retrieval," J. ACM, vol. 23, no. 2, pp. 273-286, 1976.
12. N. Vasconcelos, "Bayesian Models for Visual Information Retrieval," Ph.D. dissertation, MIT, Cambridge, 2000.
13. N. Vasconcelos, "On the complexity of probabilistic image retrieval," in Proc. IEEE Int. Conf. Computer Vision, vol. 2, Jul. 2001, pp. 400-407.
14. K. Barnard and D. Forsyth, "Learning the semantics of words and pictures," in Proc. IEEE Int. Conf. Computer Vision, vol. 2, 2001, pp. 408-415.
15. G. McLachlan and D. Peel, Finite Mixture Models. New York: Wiley, 2000.
16. J. C. Bezdek, J. Keller, R. Krisnapuram, and N. R. Pal, Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Norwell, MA: Kluwer, 1999.
17. J. Rissanen, Stochastic Complexity in Stastistical Inquiry, Singapore: World Scientific, 1989.
18. M. Figueiredo and A. K. Jain, "Unsupervised learning of finite mixture models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 3, pp. 381-396, Mar. 2002.
19. G. Schwarz, "Estimating the dimension of a model," Ann. Statist., vol. 6, pp. 461-464, 1978.
20. M. Whindham and A. Cutler, "Information ratios for validating mixture analysis," J. Roy. Statist. Soc., vol. 87, pp. 1188-1192, 1992.
21. W. Pedrycz and J. Waletzky, "Fuzzy clustering with partial supervision," IEEE Trans. Syst., Man, Cybern., B: Cybern., vol. 27, no. 5, pp. 787-795, Oct. 1997.
22. A. M. Bensaid, L. O. Hall, J. C. Bezdek, and L. P. Clarke, "Partially supervised clustering for image segmentation," Pattern Recognit., vol. 29, no. 5, pp. 859-871, May 1996.
23. Y. Wu and T. S. Huang, "Toward self-exploring discriminating features for visual learning," Eng. Applicat. Artif. Intell., vol. 15, pp. 139-150, Apr. 2002.
24. L. K. Hansen, J. Larsen, and A. Szymkowiak, "Probabilistic hierarchical clustering with labeled and unlabeled data," Int. J. Knowledge-Based Intell. Eng. Syst., vol. 6, no. 1, pp. 56-63, Jan. 2002.
25. K. Nigam, A. McCallum, S. Thrun, and T. Mitchell, "Text classification from labeled and unlabeled documents using EM," Mach. Learn., vol. 39, pp. 103-134, 1999.
26. R. N. Dave and R. Krishnapuram, "Robust clustering methods: A unified view," IEEE Trans. Fuzzy Syst., vol. 5, no. 2, pp. 270-293, May 1997.
27. R. J. Hathaway, "Another interpretation of the EM algorithm for mixture distributions," Statist. Probability Lett., vol. 4, pp. 53-56, 1986.
28. B. Thiesson, C. Meek, and D. Heckerman, "Accelerating EM for large databases," Mach. Learn., vol. 45, pp. 279-299, 2001.
29. S. Theodoridis and K. Koutroumbas, Pattern Recognition. New York: Academic, 1999.
30. C. Bohm, S. Berchtold, and D. A. Keim, "Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases," ACM Comput. Surv., vol. 33, no. 3, pp. 322-373, Sep. 2001.