最优距离聚类和特征融合表达的关键帧提取

In order to extract key frames from the monitoring video accurately and efficiently,a key frame extracting algorithm which is based on optimal distance threshold clustering and feature fusion expression is proposed. In order to obtain the frame class image set with optimal clustering number,we analyze the differences between frames of the video,and determine the optimal distance threshold which is used for unsupervised clustering of inter-frame distances. In order to extract the representative frame of each cluster,we calculate and merge color complexity and information entropy,and extracte representative frame based on'cluster average'concept. Representative frame extracted from each cluster is assigned to the key frame image set. Test results of the four monitoring videos show that,the average fidelity and average compression ratio are 96.72% and 96.42%,and the running time is shorter. Compared with the two typical algorithms based on clustering,the fidelity of the proposed algorithm is greatly improved,while the running time is smaller or equivalent,when the compression rate is the same. This algorithm solves the problem of the dependency of unsupervised clustering on the threshold and takes moving target changes and environment anomaly into account,having good performance and adaptability.