Unsupervised learning of Probabilistic Object Models (POMs) for object classification, segmentation and recognition

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Chen, Yuanhao ^a   Yuille, Alan ^b   Zhu, Long ^b   Zhang, Hongjiang ^c  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c Microsoft Adv Tech Center   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMBINED MODELING; EXPERIMENTAL ANALYSIS; IMPROVED PERFORMANCE; INTEREST POINTS; KNOWLEDGE PROPAGATION; LARGE DATASETS; OBJECT CLASSIFICATION; OBJECT MODELLING; PARAMETER LEARNING; SCALE AND ROTATION; STRUCTURE-LEARNING; UNSUPERVISED METHOD;

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); COMPUTER VISION; EDUCATION; FEATURE EXTRACTION; FOOD PROCESSING; IMAGE MATCHING; IMAGE PROCESSING; INFERENCE ENGINES; LEARNING ALGORITHMS; LEARNING SYSTEMS; LOCOMOTIVES; PARAMETER ESTIMATION; PATTERN RECOGNITION; PROBABILITY; RAILROAD CARS;

OBJECT RECOGNITION;

EID: 51949108077     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587345     Document Type: Conference Paper

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