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Volumn 5571 LNAI, Issue , 2009, Pages 246-253

Interactive image retrieval in a fuzzy framework

Author keywords

[No Author keywords available]

Indexed keywords

DESCRIPTORS; EFFICIENT LEARNING; FEATURE WEIGHT; FUZZY FEATURES; FUZZY FRAMEWORKS; IMAGE RETRIEVAL SYSTEMS; INTERACTIVE IMAGE RETRIEVAL; LOW-LEVEL FEATURES; MINIMUM COST; NUMBER OF SAMPLES; RELEVANCE FEEDBACK; SEMANTIC GAP; TRAINING SAMPLE; USER'S INTEREST; VISUAL FEATURE;

EID: 69049094134     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02282-1_31     Document Type: Conference Paper
Times cited : (3)

References (16)
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  • 8
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    • Combining intra-image and inter-class semantics for consumer image retrieval
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  • 9
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    • Content-based retrieval by relevance feedback
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    • Jin, Z.1    King, I.2    Li, X.Q.3
  • 10
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    • A novel bayesian framework for relevance feedback in image content-based retrieval systems
    • Ves, E.D., Domingo, J., Ayala, G., Zuccarello, P.: A novel bayesian framework for relevance feedback in image content-based retrieval systems. Pattern Recognition 39(9), 1622-1632 (2006)
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    • Ves, E.D.1    Domingo, J.2    Ayala, G.3    Zuccarello, P.4
  • 11
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    • Qian, F., Zhang, B., Lin, F.: Constructive learning algorithm-based rbf network for relevance feedback in image retrieval. In: Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.) CIVR 2003. LNCS, 2728, pp. 352-361. Springer, Heidelberg (2003)
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.