A Video Content Independent Mining Algorithm for Evolved Rule-based Detection of Scene Boundaries

Abstract

Segmentation of video data stream aims to divide the stream into temporally shorter, meaningful and manageable segments. This is the first step towards content-based multimedia database management, contented-based retrieval and browsing, and is very important to many other applications that aim to work with content. This paper presents a novel video mining algorithm that uses genetic programming for evolved rule-based scene boundary detection and whose key advantage is that is video content independent. Hence, the algorithm can be applied without modification to different video sequences just by feeding it different training data.