DocumentCode :
3772000
Title :
SVM Visual Classification Based on Weighted Feature of Genetic Algorithm
Author :
Dai Chunni
Author_Institution :
Dept. of Inf. Technol., Shanghai Jianqiao Coll., Shanghai, China
fYear :
2015
Firstpage :
786
Lastpage :
789
Abstract :
In order to enhance the accuracy rate of video classification, this article proposes a support SVM classification of using genetic algorithm to optimize features weighting (GA-SVM). First, this article extracts the colors and textural features of video, then adopts improved genetic algorithm to determine features weighting, and at last uses support SVM to establish video classifier and implements simulation test of corel video database. The results show that comparing with other video category algorithm, GA-SVM enhances accuracy of video classification.
Keywords :
"Support vector machines","Feature extraction","Genetic algorithms","Image color analysis","Classification algorithms","Buildings","Databases"
Publisher :
ieee
Conference_Titel :
Intelligent Systems Design and Engineering Applications (ISDEA), 2015 Sixth International Conference on
Type :
conf
DOI :
10.1109/ISDEA.2015.198
Filename :
7462735
Link To Document :
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