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Video Indexing and Retrieval in Compressed Domain Using Fuzzy-Categorization

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There has been an increased interest in video indexing and retrieval in recent years. In this work, indexing and retrieval system of the visual contents is based on feature extracted from the compressed domain. Direct possessing of the compressed domain spares the decoding time, which is extremely important when indexing large number of multimedia archives. A fuzzycategorizing structure is designed in this paper to improve the retrieval performance. In our experiment, a database that consists of basketball videos has been constructed for our study. This database includes three categories: fullcourt match, penalty and close-up. First, spatial and temporal feature extraction is applied to train the fuzzy membership functions using the minimum entropy optimal algorithm. Then, the max composition operation is used to generate a new fuzzy feature to represent the content of the shots. Finally, the fuzzy-based representation becomes the indexing feature for the content-based video retrieval system. The experimental results show that the proposal algorithm is quite promising for semantic-based video retrieval. 
WP5: Detection, Extraction and Annotation of Knowledge. 
School of Informatics University of Bradford  Ying Weng 
2007-03-06 14:30  Request for more detail


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For educational purposes only - if public document. 
Copyright School of Informatics, University of Bradford. 
 
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