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Volumn , Issue , 2004, Pages 1-6

An user preference information based kernel for SVM active learning in content-based image retrieval

Author keywords

Kernel; Probabilistic; Relevance feedback; Support vector machines

Indexed keywords

LAGRANGE MULTIPLIERS; LEARNING ALGORITHMS; MARKOV PROCESSES; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; RADIAL BASIS FUNCTION NETWORKS;

EID: 15344343101     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1026711.1026713     Document Type: Conference Paper
Times cited : (4)

References (16)
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    • Burges, C.1
  • 3
    • 0034792634 scopus 로고    scopus 로고
    • Support vector machine active learning for image retrieval
    • October
    • E. Chang and S. Tong. Support vector machine active learning for image retrieval. ACM International Conference on Multimedia, pages 107-118, October 2001.
    • (2001) ACM International Conference on Multimedia , pp. 107-118
    • Chang, E.1    Tong, S.2
  • 8
    • 0035172606 scopus 로고    scopus 로고
    • Image retrieval with relevance feedback: From heuristic weight adjustment to optimal learning methods
    • 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.
    • (2001) IEEE International Conference on Image Processing
    • Huang, T.S.1    Zhou, X.S.2
  • 9
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    • 15344345345 scopus 로고    scopus 로고
    • A kullback-leibler divergence based kernel for svm classification in multimedia applications
    • 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.
    • (2003) Neural Information Processing Systems
    • Moreno, P.J.1    Ho, P.2    Vasconceles, N.3
  • 11
    • 0032636367 scopus 로고    scopus 로고
    • Probabilistic feature relevance learning for content-based image retrieval
    • 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.
    • (1999) Computer Vision and Image Understanding , vol.75 , pp. 150-164
    • Peng, J.1    Bhanu, B.2    Qing, S.3
  • 14
    • 0036447853 scopus 로고    scopus 로고
    • Exploiting group structure to improve retrieval accuracy and speed in image databases
    • N. Vasconcelos. Exploiting group structure to improve retrieval accuracy and speed in image databases. In International conference on Image Processing, 2002.
    • (2002) International Conference on Image Processing
    • Vasconcelos, N.1
  • 15
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    • On the efficient evaluation of probabilistic similarity functions for image retrieval
    • July
    • N. Vasconcelos. On the efficient evaluation of probabilistic similarity functions for image retrieval. IEEE Trans. on Information Theory, pages 1482-1496, July 2004.
    • (2004) IEEE Trans. on Information Theory , pp. 1482-1496
    • Vasconcelos, N.1
  • 16
    • 15244364173 scopus 로고    scopus 로고
    • Relevance feedback in image retrieval: A comprehensive review
    • X. S. Zhou and T. S. Huang. Relevance feedback in image retrieval: A comprehensive review. ACM Multimedia Systems Journal, 2002.
    • (2002) ACM Multimedia Systems Journal
    • Zhou, X.S.1    Huang, T.S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.