The most commonly used method for content-based video retrieval is query by example. But the definition of video similarity brings great obstacle to further research. This paper puts forward a new approach to solve the difficulty. Firstly, it advances centroid feature vector of shot in order to reduce the storage of video database. Secondly, considering all the factors existing in human vision perception, it introduces a new comparison algorithm based on multi-granularity of video structure, which has great flexibility. Thirdly, after getting the similar video set, we take a brand-new method of feedback to adjust weight based on video similarity model. In this way, retrieval result can be optimized greatly.
基于内容的视频检索最常用的方法是示例查询。但是视频相似度的定义给进一步研究带来了很大的障碍。提出了解决这一难题的新方法。首先,它提高了镜头的质心特征向量,以减少视频数据库的存储量。其次,考虑到人类视觉感知中存在的所有因素,提出了一种基于视频结构多粒度的新比较算法,该算法具有很大的灵活性。第三,在获得相似的视频集之后,我们采用了一种全新的反馈方法来基于视频相似性模型来调整权重。这样可以极大地优化检索结果。 P>
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