Static Summarization of Video Scenes Based on Minimal Spanning Tree

Abstract

Abstract. The common practice for providing a static summarized view of a video is to create a storyboard. Storyboard is the chronological arrangement of the representative frames. Shot level storyboard suffers from redundancy as in a scene constituting shots normally repeat. Also the size of such storyboard is a constraint for many application. In this work we have considered scene as the more meaningful unit. We propose a state-based scene segmentation algorithm and also a minimal spanning tree based novel method to select the representative frames for the scenes. Storyboard consisting of scene level representative frames are much more compact than shot level storyboard. Moreover, scene being the semantic unit, flow of semantic content of the video data is well preserved. Experimental result confirms the claim.