基于视频聚类的关键帧提取算法

关键帧可以有效减少视频索引的数据量,是分析和检索视频的关键。在提取关键帧过程中,为了解决传统聚类算法对初始参数敏感的问题,提出了一种改进的基于视频聚类的关键帧提取算法。首先,提取视频帧的特征,依据帧间相似度,对视频帧进行层次聚类,并得到初始聚类结果;接着使用K-means算法对初始聚类结果进行优化,最后提取聚类的中心作为视频的关键帧。实验结果表明该方法可以大幅提高关键帧的准确率和查全率,能较好地表达视频的主要内容。

Key Frame Extraction Algorithm Based on Video Clustering

Key frame candramatically reduce the data of video indexing, and it is the fundamental processes in video analysis and video retrieval.In order to solve the problems that the traditional clustering algorithm is sensitive to the initial parameter in key frame extraction process, we propose an improved key frame extraction algorithm based on video clustering.Firstly, we extract the features of video frames. And the hierarchical clustering algorithm is used to obtain an initial clustering result, according to thesimilarity between two video frames.Then, K-means algorithm is conducted to optimize the initial clustering result and obtain the final clustering result. Finally, the center frame of each clustering is extracted as key frame. Experimental results show that the precision and recall ratio of our proposed algorithm are greatly improved. The key frames extracted by our algorithm are better to express the primary content of video.