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Volumn 2005, Issue 11099, 2005, Pages 219-225

Combining textual and visual clusters for semantic image retrieval and auto-annotation

Author keywords

clustering; Image annotation; image retrieval; semantic, C3M

Indexed keywords

ABSTRACT LEVELS; AUTO-ANNOTATION; CLUSTERING; CLUSTERING TECHNIQUES; CONTENT BASED IMAGE RETRIEVAL; COUPLING COEFFICIENT; FEATURE VECTORS; HIGH POTENTIAL; IMAGE ANNOTATION; IMAGE REGIONS; K-MEANS CLUSTERING ALGORITHM; MATRIX; NOVEL STRATEGIES; QUERY IMAGES; SEMANTIC IMAGE RETRIEVAL; TEXT ANNOTATIONS; VISUAL CLUSTERING; VISUAL FEATURE;

EID: 78651484016     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1049/ic.2005.0735     Document Type: Conference Paper
Times cited : (9)

References (15)
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  • 2
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  • 4
    • 0035440673 scopus 로고    scopus 로고
    • SIMPLIcity, Semantics-Sensitive Integrated Matching for Picture Libraries
    • James Z. Wang, Jia Li, and Gio Wiederhold: SIMPLIcity, Semantics-Sensitive Integrated Matching for Picture Libraries, IEEE Transactions on PAMI, Vol. 23, No. 9, 2001.
    • (2001) IEEE Transactions on PAMI , vol.23 , Issue.9
    • James, Z.1    Jia Li, W.2    Wiederhold, G.3
  • 5
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  • 13
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    • Formulating Semantic Image Annotation as a Supervised Learning Problem
    • G.Carneiro and N.Vasconcelos, Formulating Semantic Image Annotation as a Supervised Learning Problem, Proceedings of IEEE CVPR, 2005.
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    • Carneiro, G.1    Vasconcelos, N.2
  • 15
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    • Automatic Image Annotation and Retrieval using Cross-Media Relevence Models
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    • (2003) Proceedings of ACM SIGIR
    • Jeon, J.1    Lavrenko, V.2    Manmatha, R.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.