Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn 2004-January, Issue January, 2004, Pages

  Fei Fei, Li ^a   Fergus, Rob ^b   Perona, Pietro ^a  

^a CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; CLASSIFICATION (OF INFORMATION); COMPUTER VISION; STATISTICAL TESTS;

LEARN+; MA XIMUM LIKELIHOODS; MAXIMUM-LIKELIHOOD; OBJECT CATEGORIES; PRIOR INFORMATION; SMALL TRAINING; TRAINING EXAMPLE; TRAINING IMAGE; TRAINING SETS; VISUAL MODEL;

MAXIMUM LIKELIHOOD;

EID: 84932617705     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2004.383     Document Type: Conference Paper

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