提取多场景视频关键帧的复合HOG特征聚类方法

Composite HOG Feature Clustering Method for Extracting Key Frames of Multi-Scene Video

由于直接利用帧差数据提取动态多场景视频关键帧往往会产生过多冗余帧,方向梯度直方图(HOG)特征对图像亮度、场景变化具有较好的稳定性。为此,提出了用于提取多场景视频关键帧的复合HOG特征聚类方法来提升关键帧提取效率。首先,通过提取视频帧的HOG特征引入图像信息熵构成复合特征矢量,以保持数据特征相关性。其次,根据复合特征矢量统计视频帧间差异数据确定视频分割镜头、关键帧提取个数;再次,分别考虑镜头内帧集合和完整视频帧集合,无重复地将信息熵较大的视频帧选为初始聚类中心以引导聚类算法搜索方向,并通过K均值聚类抽取视频关键帧。与传统K均值聚类方法比较后发现,所提算法冗余度降低0.003~0.015,查准率提高了0.14~0.21,聚类时间得到下降,精度和效率较优。

Due to the fact that extracting dynamic multi scene video keyframes directly from frame difference data often results in excessive redundant 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 keyframe extraction.Firstly,by extracting the HOG features of video frames and introducing image information entropy,a composite feature vector is constructed to maintain the correlation of data features.Secondly,based on the composite feature vector,the difference data between video 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 repetition 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.