PCA-SVM Based Vehicle Color Recognition 


Vol. 15,  No. 4, pp. 285-292, Aug.  2008
10.3745/KIPSTB.2008.15.4.285


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  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.

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[IEEE Style]

S. M. Park and K. J. Kim, "PCA-SVM Based Vehicle Color Recognition," The KIPS Transactions:PartB , vol. 15, no. 4, pp. 285-292, 2008. DOI: 10.3745/KIPSTB.2008.15.4.285.

[ACM Style]

Sun Mi Park and Ku Jin Kim. 2008. PCA-SVM Based Vehicle Color Recognition. The KIPS Transactions:PartB , 15, 4, (2008), 285-292. DOI: 10.3745/KIPSTB.2008.15.4.285.

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