Exploiting the entire feature space with sparsity for automatic image annotation

MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops

Volumn , Issue , 2011, Pages 283-292

  Ma, Zhigang ^a   Yang, Yi ^b   Nie, Feiping ^c   Uijlings, Jasper ^a   Sebe, Nicu ^a  

^a UNIVERSITY OF TRENTO   (Italy)

^b CARNEGIE MELLON UNIVERSITY   (United States)

^c UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

Author keywords

Image annotation; Manifold learning; Semi supervised learning; Sparse feature selection

Indexed keywords

AUTOMATIC IMAGE ANNOTATION; DATA POINTS; DIGITAL IMAGE; EXPLOSIVE GROWTH; FEATURE SPACE; IMAGE ANNOTATION; IMAGE DATABASE; IMAGE MANAGEMENT; ITERATIVE ALGORITHM; LABELED AND UNLABELED DATA; MANIFOLD LEARNING; OBJECTIVE FUNCTIONS; REAL-WORLD IMAGE; SEMANTIC CONCEPT; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING;

ALGORITHMS; FEATURE EXTRACTION; IMAGE ANALYSIS; SEMANTICS; SUPERVISED LEARNING;

IMAGE RETRIEVAL;

EID: 84455161745     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2072298.2072336     Document Type: Conference Paper

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