RPCA-KFE: Key Frame Extraction for Consumer Video based Robust Principal Component Analysis

Key frame extraction algorithms consider the problem of selecting a subset of the most informative frames from a video to summarize its content. Several applications such as video summarization, search, indexing and prints from video can benefit from extracted key frames of the video under consideration. Most approaches in this class of algorithms work directly with the input video dataset, without considering the underlying low rank structure of the dataset. Other algorithms exploit the low rank component only; ignoring the other key information in the video. In this paper, a novel key frame extraction framework based on Robust Principal Component Analysis (RPCA) is proposed. Furthermore, we target the challenging application of extracting key frames from unstructured consumer videos. The proposed framework is motivated by the observation that RPCA decomposes an input data into (a) a low rank component that reveals the systematic information across the elements of the dataset and (b) a set of sparse components each of which containing distinct information about each element in the same dataset. The two information types are combined into a single `1-norm based non-convex optimization problem to extract the desired number of key frames. Moreover, we develop a novel iterative algorithm to solve this optimization problem. The proposed RPCA-based framework does not require shot(s) detection, segmentation, or semantic understanding of the underlying video. Finally, experiments are performed on a variety of consumer videos. A comparison of the results obtained by our method with the ground truth and related state-of-the-art algorithms clearly indicates the viability of the proposed RPCAbased framework.