Multi-View Video Summarization Using Bipartite Matching Constrained Optimum-Path Forest Clustering

Abstract

The task of multi-view video summarization is to efficiently represent the most significant information from a set of videos captured for a certain period of time by multiple cameras. The problem is highly challenging because of the huge size of the data, presence of many unimportant frames with low activity, inter-view dependencies, and significant variations in illumination. In this paper, we propose a graph-theoretic solution to the above problems. Semantic feature in form of visual bag of words and visual features like color, texture, and shape are used to model shot representative frames after temporal segmentation . Gaussian entropy is then applied to filter out frames with low activity. Inter-view dependencies are captured via bipartite graph matching. Finally, the optimum-path forest algorithm is applied for the clustering purpose. Subjective as well as objective evaluations clearly indicate the effectiveness of the proposed approach.