Video Hierarchical Structure Mining

To structuralize video streams plays an important role in the processing of video. The basic structure for video is a hierarchical structure which consists of four kinds of components, namely frame, shot, scene, and video program. A simple framework for video hierarchical structure mining is to partition continuous video frames into discrete physical shots, extract features from video shots and construct scene structure based on shots. In this paper, two crucial algorithms of video hierarchical structure mining, multi-features shot clustering (MSC) and scene change detection (SCD), are proposed based on color, texture and semantic similarity of shot. Our experimental results demonstrate the performance of SCD is better than that of MSC