基于聚类的视频镜头分割和关键帧提取

Video shot segmentation and key frame extraction based on clustering

镜头分割是基于内容的视频检索和浏览首先要解决的关键技术。视频分割为镜头后,下一步的工作就是进行关键帧提取,用以描述镜头的主要内容。提出了一种改进的基于聚类的镜头分割和关键帧提取算法,在无监督聚类中引入一个参考变量,解决了利用无监督聚类进行镜头分割和关键帧提取时可能产生的帧序不连续或分割错误的问题。在关键帧提取阶段,将镜头分割为子镜头后,引入图像熵的概念提取关键帧。实验结果表明了改进算法在镜头分割和关键帧提取方面的有效性。

Shot segmentation is a vital technology that must be resolved firstly in video retrieval and browse. Then key frame extraction will be carried out after shot segmentation to describe main content of shot. An improved algorithm for shot segmentation and key frame extraction based on clustering is proposed, a referenced variable is used in unsupervised clustering to resolve the frame sequence's incontinuity or false segmentation problems which can be caused probably by unsupervised clustering. During key frame extraction, the concept of image entropy is used after shot being segmented into subshots. Experimental results demonstrate the efficiency of the improved algorithm in shot segmentation and key frame extraction.