Static Video Summarization: A Comparative Study of Clustering-Based Techniques

The spectacular increase of video data, due to the availability of low cost and large storage enabled video capturing devices, has led to problems of indexing and browsing videos. In the past two decades, video summarization has evolved as a solution to cope up with challenges imposed by big video data. Video summarization deals with identification of relevant and important frames or shots for efficient storage, indexing, and browsing of videos. Among various approaches, clustering-based methods have gained popularity in the field of video summarization owing to their unsupervised nature that makes the process independent of the need for the expensive and tedious task of obtaining annotations for videos. This study is an attempt to comprehensively compare various clustering-based unsupervised machine learning techniques along with evaluation of performance of selective local and global features in video summarization. Quantitative evaluations are performed to indicate the effectiveness of global features—color and texture as well as local features—SIFT along with six clustering methods of different nature. The proposed models are empirically evaluated on the Open Video (OV) dataset, a standard video summarization dataset for static video summarization.