A Comparative Study of Unsupervised Video Shot Boundary Detection Techniques Using Probabilistic Fuzzy Entropy Measures

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Author(s): Biswanath Chakraborty (RCC Institute of Information Technology, India), Siddhartha Bhattacharyya (RCC Institute of Information Technology, India)and Susanta Chakraborty (Bengal Engineering and Science University, India)
Copyright: 2013
Pages: 13
Source title: Handbook of Research on Computational Intelligence for Engineering, Science, and Business
Source Author(s)/Editor(s): Siddhartha Bhattacharyya (RCC Institute of Information Technology, India)and Paramartha Dutta (Visva-Bharati University, India)
DOI: 10.4018/978-1-4666-2518-1.ch009

Keywords: Artificial Intelligence / Computational Intelligence / Computer Science & IT / Engineering Science Reference

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Abstract

The performance of video shot boundary detection technique in unsupervised video sequence can be improved by the use of different probabilistic fuzzy entropies. In this chapter, the authors present a new technique for identifying as to whether there are any appreciable changes from one video context to another in the available sequence of image frames extracted from a mixture of a numbers of video files. They then compared their technique with an existing technique and found improved performance of the video shot boundary detection techniques using probabilistic fuzzy entropies.

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A Comparative Study of Unsupervised Video Shot Boundary Detection Techniques Using Probabilistic Fuzzy Entropy Measures

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