A stochastic graph grammar for compositional object representation and recognition

Pattern Recognition

Volumn 42, Issue 7, 2009, Pages 1297-1307

  Lin, Liang ^a,b,c   Wu, Tianfu ^b,c   Porway, Jake ^c   Xu, Zijian ^c  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

^b Lotus Hill Research Institute   (China)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

And Or graph model; Object recognition; Recursive inference

Indexed keywords

CONTEXT FREE GRAMMARS; CONTEXT SENSITIVE GRAMMARS; FEATURE EXTRACTION; FORMAL LANGUAGES; GRAPH THEORY; HIDDEN MARKOV MODELS; OBJECT RECOGNITION; RANDOM PROCESSES; REAL TIME SYSTEMS; RECURSIVE FUNCTIONS;

AND-OR GRAPH MODEL; ATTRIBUTE GRAPH GRAMMARS; CALTECH; DATA-SETS; GENERATIVE MODELS; GRAMMAR RULES; MARKOV RANDOM FIELDS; OBJECT REPRESENTATIONS; PROBABILISTIC FORMULATIONS; RECURSIVE INFERENCE; SPATIAL RELATIONSHIPS; STATE-OF-ART METHODS; STOCHASTIC CONTEXT-FREE GRAMMARS; STOCHASTIC GRAPHS; TOP DOWNS; TRAINING AND TESTING;

STOCHASTIC MODELS;

EID: 62349137210     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2008.10.033     Document Type: Article

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