Video summarization using singular value decomposition

TLDR

The authors propose a novel video summarization technique based on singular value decomposition. They construct a feature‑frame matrix, apply SVD to obtain a refined feature space, cluster visually similar frames, and use the most static cluster’s context value as a threshold to segment the video. The method produces concise keyframe sets or motion summaries with minimal redundancy and balanced coverage of content.

Abstract

The authors propose a novel technique for video summarization based on singular value decomposition (SVD). For the input video sequence, we create a feature-frame matrix A, and perform the SVD on it. From this SVD, we are able, to not only derive the refined feature space to better cluster visually similar frames, but also define a metric to measure the amount of visual content contained in each frame cluster using its degree of visual changes. Then, in the refined feature space, we find the most static frame cluster, define it as the content unit, and use the context value computed from it as the threshold to cluster the rest of the frames. Based on this clustering result, either the optimal set of keyframes, or a summarized motion video with the user specified time length can be generated to support different user requirements for video browsing and content overview. Our approach ensures that the summarized video representation contains little redundancy, and gives equal attention to the same amount of contents.