MSKVS: Adaptive mean shift-based keyframe extraction for video summarization and a new objective verification approach

Video abstraction is an interesting topic that aims at briefly representing the entire video stream by producing a short summary either statically or dynamically. In this paper, we present an optimal static video summarization method based on keyframe extraction, termed as MSKVS. The proposed MSKVS has three major components: A new feature representation is exploited to describe the visual content of the video, a simple and fast algorithm is proposed to eliminate most similar and redundant frames, and an adaptive mean shift algorithm is used to select the most representative keyframes. We further develop a novel verification technique to measure the amount of information preserved by the produced summary and to make sure that it deserves to present the entire video stream regardless of human opinion impact. We report experimental results on six challenging datasets using different evaluation metrics, showing that MSKVS achieves state-of-the-art performances in a short computation time.