Semantic Similarity Based Video Reranking

With the popularity of Internet video, video retrieval applications become more and more widespread. The effect of the traditional retrieval optimization algorithms cannot meet the needs of users. To improve rearrangement semantic rationality of video search results, this paper introduces the video annotation to mark based on the video content objectively. At the same time, the use of words semantic knowledge tree is important. Use the video annotation text and search terms in order to quantify the performance of the similarity. And combine video title and related text information to get the final video reranking sequence. In this paper, the validity was validated by experimental method, this paper also add video test on network video. Experimental results show that the performance of the method of reranking is improved.