Certain investigations on video scene segementaion techniques

Abstract

In this paper we propose temporal and optimized video scene segmentation techniques. Temporal video scene segmentation methods aims to partition the video to elementary image sequences termed scenes. The main purpose of video scene segmentation is to extract objects from series of consecutive video frames. One important goal in video analysis is to group the shots, such that all shots in a single scene are related to the same subject. In this paper we make an analysis on four methods of video scene segmentation. The methods proposed for segmentation is Backward shot coherence(BSC), Best first model merging(BFMM) and Markov chain Monte Carlo (MCMC) and Hierarchical chain partitioning(HCP). Video scene segmentation is performed by considering both content coherence and temporally contextual dissimilarity. This task is formulated as a ratio function of content coherence within a scene and contextual dissimilarity of adjacent scenes. Finally we propose an optimized video scene segmentation which is a novel approach that is used to segment the video scene and thereby optimize them to produce efficiency. An effective and efficient hierarchical chain partitioning(HCP) approach is used to find the optimal scene segmentation. HCP gets competitive performance at low tolerance and better performance at high tolerance The HCP method approaches 100 % performance at tolerance = 1. The output segments can be used for further analysis and processing of the videos that is for indexing, storage, searching and retrieval of particular portion of video. It can be used in several applications in the fields of Image processing, video content analysis, Computer vision, Movies, Medical, Sports, and News. We have tested the proposed methods in Feature Films, News videos, TV Shows and high degree of accuracy has been obtained.