Scalable Video Summarization Using Skeleton Graph and Random Walk

Scalable video summarization has emerged as an important problem in present day multimedia applications. Effective summaries need to be provided to the users for videos of any duration at low computational cost. In this paper, we propose a framework which is scalable during both the analysis and the generation stages of video summarization. The problem of scalable video summarization is modeled as a problem of scalable graph clustering and is solved using skeleton graph and random walks in the analysis stage. A cluster significance factor-based ranking procedure is adopted in the generation stage. Experiments on videos of different genres and durations clearly indicate the supremacy of the proposed method over a recently published work.