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Content Coverage and Redundancy Removal in Video Summarization
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Author(s): Hrishikesh Bhaumik (RCC Institute of Information Technology, India), Siddhartha Bhattacharyya (RCC Institute of Information Technology, India)and Susanta Chakraborty (Indian Institute of Engineering Science and Technology, India)
Copyright: 2017
Pages: 23
Source title:
Intelligent Analysis of Multimedia Information
Source Author(s)/Editor(s): Siddhartha Bhattacharyya (RCC Institute of Information Technology, India), Hrishikesh Bhaumik (RCC Institute of Information Technology, India), Sourav De (The University of Burdwan, India)and Goran Klepac (University College for Applied Computer Engineering Algebra, Croatia & Raiffeisenbank Austria, Croatia)
DOI: 10.4018/978-1-5225-0498-6.ch013
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Abstract
Over the past decade, research in the field of Content-Based Video Retrieval Systems (CBVRS) has attracted much attention as it encompasses processing of all the other media types i.e. text, image and audio. Video summarization is one of the most important applications as it potentially enables efficient and faster browsing of large video collections. A concise version of the video is often required due to constraints in viewing time, storage, communication bandwidth as well as power. Thus, the task of video summarization is to effectively extract the most important portions of the video, without sacrificing the semantic information in it. The results of video summarization can be used in many CBVRS applications like semantic indexing, video surveillance copied video detection etc. However, the quality of the summarization task depends on two basic aspects: content coverage and redundancy removal. These two aspects are both important and contradictory to each other. This chapter aims to provide an insight into the state-of-the-art approaches used for this booming field of research.
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