PICS: A Novel Technique for Video Summarization

Abstract

With brisk growth in video data the demand for both effective and powerful methods for video summarization is also elevated so that the users can browse the apace and comprehend a large amount of video content. Our paper highlights a novel keyframe extraction technique based on the clustering to attain video summarization (VS). Clustering is an unsupervised procedure and these algorithms rely on some prior assumptions to define subgroups in the given dataset. To extract the keyframes in a video, a procedure called k-medoids clustering is used. To find the number of optimal clusters, is a challenge here. This task can be achieved by using the cluster validation procedure, Calinski–Harabasz index (CH index). This procedure is based on the well-defined criteria for clustering that enable the selection of an optimal parameter value to get the best partition results for the dataset. Thus, CH index allows users a parameter independent VS approach to select the keyframes in video without bringing down the further computational cost. The quantitative and qualitative evaluation and the computational complexity are drained to compare the achievements of our proposed model with the state-of-the-art models. The experimental results on two standard datasets having various categories of videos indicate PICS model outperforms other existing models with best F-measure.