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.
This paper is published in SPIE Conference on Multimedia Content Analysis, Management and Retrieval 2006, January 17-19, 2006, San Jose, USA / SPIE proceedings Volume 6073
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