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http://dx.doi.org/10.3745/KIPSTB.2008.15-B.4.285

PCA-SVM Based Vehicle Color Recognition  

Park, Sun-Mi (경북대학교 전자전기컴퓨터학부)
Kim, Ku-Jin (경북대학교 컴퓨터공학과)
Publication Information
The KIPS Transactions:PartB / v.15B, no.4, 2008 , pp. 285-292 More about this Journal
Abstract
Color histograms have been used as feature vectors to characterize the color features of given images, but they have a limitation in efficiency by generating high-dimensional feature vectors. In this paper, we present a method to reduce the dimension of the feature vectors by applying PCA (principal components analysis) to the color histogram of a given vehicle image. With SVM (support vector machine) method, the dimension-reduced feature vectors are used to recognize the colors of vehicles. After reducing the dimension of the feature vector by a factor of 32, the successful recognition rate is reduced only 1.42% compared to the case when we use original feature vectors. Moreover, the computation time for the color recognition is reduced by a factor of 31, so we could recognize the colors efficiently.
Keywords
Principal components analysis; dimension reduction; color recognition; color histogram;
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