Application of clustering techniques for video summarization - An empirical study

Abstract

Identification of relevant frames from a video which can then be used as a summary of the video itself, is a challenging task. An attempt has been made in this study to empirically evaluate the effectiveness of data mining techniques in video summarization. Video Summarization systems based on histogram and entropy features extracted from three different color spaces: RGB, HSV and YCBCR and clustered using K-Means, FCM, GM and SOM were empirically evaluated on fifty video datasets from the VSUMM [1] database. Results indicate that clustering based video summarizations techniques can be effectively used for generating video summaries.