Video Summarization using Low-Rank Sparse Representation

Abstract

Extraction of key frames plays a significant role in selecting the most informative subset of frames from a huge amount of video data in order to compress its content. Key frame extraction has a good number of applications, such as video browsing, indexing, and storage. Most key framebased video summarization techniques directly process an input video dataset. An alternative approach uses only low-rank components, without considering the rest of the significant information in the video. This paper presents a novel key frame-extraction framework based on low-rank sparse representation. The proposed framework is motivated by the fact that low-rank sparse-feature representation pursues consistent nonlocal structures for image pixels with similar features. We use low-rank representation to ensure globally consistent non-salient systematic structures for pixels with similar features, and we also use sparse representation to robustly select the best sample for distinct structures of all pixels. Experimental results on a human-labeled benchmark dataset and a comparative performance evaluation with state-of-the-art methods demonstrate the advantages of the proposed method.