关键词: 层次聚类算法, 感知哈希, [k]-means, 尺度不变特征变换（SIFT）, BOF算法, 语义相关

Abstract:

Key frame extraction is an important part of video summarization. The quality of key frame extraction directly affects people’s understanding of video. Most of the traditional key frame extraction algorithms are based on visual correlation, that is, only extracting the underlying information to calculate its similarity, ignoring the semantic correlation, which is easy to cause error and some redundancy. In this paper, a semantic-based video key frame extraction algorithm is proposed. Firstly, the algorithm uses hierarchical clustering algorithm to extract video key frames. Then, a semantic correlation algorithm is used to get rid of the redundant frames by histogram comparison. Finally, compared with other algorithms, the proposed algorithm has a relatively small redundancy.

Key words: hierarchical clustering algorithm, Hasche awareness, [k]-means, Scale Invariant Feature Transform（SIFT）, Bag of Feature（BOF）, semantic correlation