DocumentCode :
1894095
Title :
Research and Design of Image Feature Recognition Classifier Based on SVM
Author :
Kai Song ; Yu-Liang Chang
Author_Institution :
Dept. of info Sci. & Eng., Shenyang Ligong Univ., Shenyang, China
Volume :
1
fYear :
2009
fDate :
10-11 Oct. 2009
Firstpage :
666
Lastpage :
669
Abstract :
It built a classifier, which could improve recognition rate of image feature. Based on statistical learning theory, it built a classifier on support vector machine (SVM), and determined the parameter of SVM and Guass radial kernel function. In the experiment, classifier of SVM was trained by feature sample, then carried on classifying , recognition and detection. The result of simulation showed that classification based on support vector machine (SVM) not only has better robustness, but also effectively improve the recognition rate and decrease false recognition rate .
Keywords :
feature extraction; image classification; image sampling; radial basis function networks; support vector machines; Guass radial kernel function; SVM; image feature recognition classifier; statistical learning theory; support vector machine; Design automation; Design engineering; Image recognition; Kernel; Machine learning; Research and development; Solids; Statistical learning; Support vector machine classification; Support vector machines; classifier; image features; recognition rate; statistical learning; support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
Conference_Location :
Changsha, Hunan
Print_ISBN :
978-0-7695-3804-4
Type :
conf
DOI :
10.1109/ICICTA.2009.166
Filename :
5287565
Link To Document :
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