A Model-Based Shot Boundary Detection Technique Using Frame Transition Parameters

2012 ◽  
Vol 14 (1) ◽  
pp. 223-233 ◽  
Author(s):  
Partha Pratim Mohanta ◽  
Sanjoy Kumar Saha ◽  
Bhabatosh Chanda
Keyword(s):  
Boundary Detection ◽  
Detection Technique ◽  
Shot Boundary Detection ◽  
Model Based ◽  
Shot Boundary ◽  
Frame Transition

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

2013 ◽  
pp. 228-240 ◽  
Author(s):  
Biswanath Chakraborty ◽  
Siddhartha Bhattacharyya ◽  
Susanta Chakraborty
Keyword(s):  
Boundary Detection ◽  
Fuzzy Entropy ◽  
Detection Technique ◽  
Shot Boundary Detection ◽  
Detection Techniques ◽  
Entropy Measures ◽  
Video Shot ◽  
Shot Boundary ◽  
Improved Performance ◽  
A New Technique

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.

Generative Model Based Video Shot Boundary Detection for Automated Surveillance

2018 ◽  
Vol 9 (4) ◽  
pp. 69-95 ◽  
Author(s):  
Biswanath Chakraborty ◽  
Siddhartha Bhattacharyya ◽  
Susanta Chakraborty
Keyword(s):  
Video Processing ◽  
Contextual Information ◽  
Boundary Detection ◽  
Gaussian Mixture ◽  
Generative Model ◽  
Shot Boundary Detection ◽  
Support Vector ◽  
Model Based ◽  
Video Shot ◽  
Shot Boundary

Video shot boundary detection (SBD) or video cut detection is one of the fundamental processes of video-processing with respect to semantic understanding, contextual information accumulation, labeling, content-based information retrieval and many more applications, such as video surveillance and monitoring. In this work, the authors have proposed a generative-model based framework for detecting shot boundaries in between the frames of a video segment. To generate a model of shot-boundaries, the authors have applied the concepts of Support Vector Machine to estimate the distance between any two images, and then, have generated a Gaussian Mixture Model from the estimated distances. Next, a Bayesian Estimation process checks the presence of boundaries in between the images by exploiting the Gaussian Mixture-based boundary model. Further, the authors have used the principles of Compressive Sensing to reduce the overhead of boundary detection process without losing of important information.

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