Key frame extraction from consumer videos using epitome

Abstract

Key frame extraction algorithms select a subset of the most informative frames from videos. Key frame extraction finds applications in several broad areas of video processing research such as video summarization, video indexing, and prints from video. In this paper, an image epitome [1][2] based method to extract key frames from unstructured consumer videos is presented. In the proposed approach, we exploit image epitome to measure dissimilarity between frames of the input video. The dissimilarity scores are further analyzed using a min-max approach to extract the desired number of key frames from the input video. The proposed approach does not require shot(s) detection, segmentation, or semantic understanding. A comparison of the results obtained by this method with the ground truth agreed by multiple judges clearly indicates the feasibility of the proposed approach.