Abstract
Traditional content-based video retrieval algorithms usually only used the low-level features of video images, and the content description is not enough, which leads to the unsatisfactory Retrieval results. This paper studied how to combine the low-level features and semantic features of video, improved the existing index structure, and designed an efficient sports video retrieval algorithm. The experimental results show that the proposed algorithm can meet the real-time requirements and improve the accuracy and recall rate compared with the existing methods. In addition, the proposed sports video index combined with semantic features is better than the existing index in both query time and query results.
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