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16 January 2006 Semantic feature extraction with multidimensional hidden Markov model
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Abstract
Conventional block-based classification is based on the labeling of individual blocks of an image, disregarding any adjacency information. When analyzing a small region of an image, it is sometimes difficult even for a person to tell what the image is about. Hence, the drawback of context-free use of visual features is recognized up front. This paper studies a context-dependant classifier based on a two dimensional Hidden Markov Model. In particular we explore how the balance between structural information and content description affect the precision in a semantic feature extraction scenario. We train a set of semantic classes using the development video archive annotated by the TRECVid 2005 participants. To extract semantic features the classes with maximum a posteriori probability are searched jointly for all blocks. Preliminary results indicate that the performance of the system can be increased by varying the block size.
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Joakim Jiten, Bernard Merialdo, and Benoit Huet "Semantic feature extraction with multidimensional hidden Markov model", Proc. SPIE 6073, Multimedia Content Analysis, Management, and Retrieval 2006, 60730N (16 January 2006); https://doi.org/10.1117/12.650590
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