DocumentCode :
2016400
Title :
A improved k-means clustering algorithm combined with the genetic algorithm
Author :
Li, Xiaoping ; Zhang, Lei ; Li, Yinxiang ; Wang, Zhenghong
Author_Institution :
Dept. of Comput. Sci. & Technol., Beijing Inst. of Technol., Beijing, China
fYear :
2010
fDate :
16-18 Aug. 2010
Firstpage :
121
Lastpage :
124
Abstract :
The cluster approach is one of the basic methods to pattern classification and system modeling. The clustering target is that according to some rules, divide the sample data set in the sample space into some subsets indicating different patterns or system behavior[1]. In the course of establishing the video image indexing, every step from building index according to the basic visual characters of extracted images, to forming category index trough extracting related programs, uses the clustering thought. So, how to choose a suitable effective clustering algorithm will directly affect the efficiency to establish the video image indexing and the performance of the whole management system. The k-means clustering algorithm is a relatively good one.
Keywords :
feature extraction; genetic algorithms; indexing; pattern classification; pattern clustering; video databases; video signal processing; K-means clustering algorithm; genetic algorithm; image extraction; pattern classification; sample data set; system modeling; video image indexing; Algorithm design and analysis; Biological cells; Indexes; genetic algorithm; k-means; k-means clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Content, Multimedia Technology and its Applications (IDC), 2010 6th International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4244-7607-7
Electronic_ISBN :
978-8-9886-7827-5
Type :
conf
Filename :
5568718
Link To Document :
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