A Novel Shot Boundary Detection Method Based on Genetic Algorithm-Support Vector Machine

2011 ◽  
Author(s):  
Xuemei Sun ◽  
Long Zhao ◽  
Mingwei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Detection Method ◽  
Boundary Detection ◽  
Shot Boundary Detection ◽  
Support Vector ◽  
Shot Boundary

scholarly journals Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Zinah N. Idan ◽  
Sadiq H. Abdulhussain ◽  
Basheera M. Mahmmod ◽  
Khaled A. Al-Utaibi ◽  
S.A.R. Al-Hadad ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Boundary Detection ◽  
Shot Boundary Detection ◽  
Support Vector ◽  
Shot Boundary

scholarly journals Enhanced Video Classification System Using a Block-Based Motion Vector

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 499
Author(s):  
Jayasree K ◽  
Sumam Mary Idicula
Keyword(s):  
Support Vector Machine ◽  
Classification System ◽  
Boundary Detection ◽  
Video Data ◽  
Shot Boundary Detection ◽  
Support Vector ◽  
Video Classification ◽  
Video Clips ◽  
Shot Boundary ◽  
Block Based

The main objective of this work was to design and implement a support vector machine-based classification system to classify video data into predefined classes. Video data has to be structured and indexed for any video classification methodology. Video structure analysis involves shot boundary detection and keyframe extraction. Shot boundary detection is performed using a two-pass block-based adaptive threshold method. The seek spread strategy is used for keyframe extraction. In most of the video classification methods, selection of features is important. The selected features contribute to the efficiency of the classification system. It is very hard to find out which combination of features is most effective. Feature selection makes relevance to the proposed system. Herein, a support vector machine-based classifier was considered for the classification of video clips. The performance of the proposed system considered six categories of video clips: cartoons, commercials, cricket, football, tennis, and news. When shot level features and keyframe features, along with motion vectors, were used, 86% correct classification was achieved, which was comparable with the existing methods. The research concentrated on feature extraction where combination of selected features was given to a classifier to get the best classification performance.

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