Parameter free clustering approach for event summarization in videos

Abstract

Digital videos, nowadays, are becoming more common in various fields like education, entertainment, etc. due to increased computational power and electronic storage capacity. With an increasing size of video collection, a technology is needed to effectively and efficiently browse through the video without losing contents of the video. The user may not always have sufficient time to watch the entire video or the entire content of the video may not be of interest of user. In such cases, user may just want to go through the summary of the video instead of watching the entire video. In this paper we propose an approach for event summarization in videos based on clustering method. Our proposed method provides a set of key frames as a summary for a video. The key frames which are closer to the cluster heads of the optimal clustering are combined to form the summarized video. The evaluation of the proposed model is done on a publicly available dataset and compared with ten state-of-the-art models in terms of precision, recall and F-measure. The experimental results demonstrate that the proposed model outperforms the rest in terms of F-measure.