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The use of genetic algorithm for feature selection in video concept detection

Momtazpour, M, Saraee, M and Palhang, M 2010, The use of genetic algorithm for feature selection in video concept detection , in: The 18th Iranian Conference on Electrical Engineering (ICEE), 2010, 11-13 May 2010, Isfahan Iran.

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Video semantic concept detection is considered as an important research problem by the multimedia industry in recent years. Classification is the most accepted method used for concept detection, where, the output of the classification system is interpreted as semantic concepts. These concepts can be employed for automatic indexing, searching and retrieval of video objects. However, employed features have high dimensions and thus, concept detection with the existing classifiers experiences high computation complexity. In this paper, a new approach is proposed to reduce the classification complexity and the required time for learning and classification by choosing the most important features. For this purpose genetic algorithms are employed as a feature selector. Simulation results illustrate improvements in the behavior of the classifier.

Item Type: Conference or Workshop Item (Paper)
Themes: Built and Human Environment
Media, Digital Technology and the Creative Economy
Schools: Schools > College of Science & Technology > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: Proceedings of the 18th Iranian Conference on Electrical Engineering (ICEE), 2010
Publisher: IEEE
Refereed: Yes
Depositing User: Dr Mo Saraee
Date Deposited: 03 Nov 2011 15:25
Last Modified: 29 Oct 2015 00:11

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