Key frame extraction from consumer videos using sparse representation

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, creating "chapter titles" in DVDs, video indexing, and prints from video. In this paper, a sparse representation based method to extract key frames from unstructured consumer videos is presented. In the proposed approach, video frames are projected to a low dimensional random feature space and theory from sparse signal representation is used to analyze the spatio-temporal information of the video data and generate key frames. The proposed approach is computationally efficient and 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 and another approach based on camera operator's intent clearly indicates the feasibility of the proposed approach.