Reducing redundancy in videos using reference frame and clustering technique of key frame extraction

Abstract

Digital video is becoming an emerging force in current computer and telecommunication industries for its large mass of data. Video segmentation and key-frame extraction have become crucial for the development of advanced digital video systems. Key frame extraction is a very useful technique to provide a concise access to the video content and is the first step towards efficient browsing and retrieval in video databases. Existing approaches are either computationally expensive or ineffective in capturing salient visual content. The proposed system extracts key frames from input videos using two distinct, cost-effective algorithms namely reference based key frame extraction and clustering. It uses multiple characteristics such as co-relation, optical flow and mutual information to identify and extract key frames. The proposed system is able to extract the key frames efficiently for any video format & the extracted key frames can satisfactorily represent the salient content of the video. Storage is reduced by one-eighth of the total space required by the original video and the original content can be represented in one-fourth the time of the input video achieving very high compression efficiency & hence can be used in any video retrieval applications.