Composite HOG Feature Clustering Method for Extracting Key Frames of Multi-Scene Video
Due to the fact that extracting dynamic multi scene video keyframes directly from frame difference data often results in excessive re-dundant frames,the directional gradient histogram(HOG)feature has good stability for image brightness and scene changes.Therefore,a composite HOG feature clustering method for extracting keyframes from multi scene videos has been proposed to improve the efficiency of key-frame extraction.Firstly,by extracting the HOG features of video frames and introducing image information entropy,a composite feature vec-tor is constructed to maintain the correlation of data features.Secondly,based on the composite feature vector,the difference data between vid-eo frames is calculated to determine the number of video segmentation shots and keyframe extractions;Again,considering both the intra shot frame set and the complete video frame set,select the video frames with high information entropy as the initial clustering centers without repeti-tion to guide the clustering algorithm search direction,and extract video keyframes through K-means clustering.Compared with the traditional K-means clustering method,it was found that the proposed algorithm reduces redundancy by 0.003～0.015,improves precision by 0.14～0.21,reduces clustering time,and has better accuracy and efficiency.
万方数据
维普
