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Volumn , Issue , 2009, Pages 224-230

Context-based multi-label image annotation

Author keywords

Automatic image annotation; Kernel methods; Keyword propagation; Multilabel learning; Visual keywords

Indexed keywords

2D STRING; AUTOMATIC IMAGE ANNOTATION; CONTEXT-BASED; IMAGE ANNOTATION; KERNEL METHODS; KEYWORD PROPAGATION; LEARNING PROBLEM; MULTI-LABEL; RELEVANCE MODELS; SEQUENCE SIMILARITY; SPATIAL SPECTRA; STANDARD IMAGES; STATE-OF-THE-ART METHODS; TEST IMAGES; TRAINING IMAGE;

EID: 74049137018     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1646396.1646434     Document Type: Conference Paper
Times cited : (19)

References (25)
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    • E. Chang, G. Kingshy, G. Sychay, and G. Wu. CBSA: Content-based soft annotation for multimodal image retrieval using Bayes point machines. IEEE Trans. on Circuits and Systems for Video Technology, 13(1):26-38, 2003.
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  • 7
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    • Hofmann, T.1
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    • 0141836773 scopus 로고    scopus 로고
    • Automatic linguistic indexing of pictures by a statistical modeling approach
    • J. Li and J. Wang. Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans. on Pattern Analysis and Machine Intelligence, 25(9):1075-1088, 2003.
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    • Li, J.1    Wang, J.2
  • 16
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    • Image annotation via graph learning
    • J. Liu, M. Li, Q. Liu, H. Lu, and S. Ma. Image annotation via graph learning. Pattern Recognition, 42(2):218-228, 2009.
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    • A graph-based image annotation framework
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    • TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context
    • J. Shotton, J. Winn, C. Rother, and A. Criminisi. TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. International Journal of Computer Vision, 81(1):2-23, 2009.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.