PCA-SVM 기법을 이용한 차량의 색상 인식
PCA-SVM Based Vehicle Color Recognition
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초록
색상 히스토그램은 영상의 색상 특징을 표현하기 위한 특징 벡터로 빈번히 사용되지만, 고차원의 특징 벡터를 생성하므로 효율성의 면에서 한계점을 갖고 있다. 본 논문에서는 주어진 차량 영상의 색상 히스토그램에 PCA (principal components analysis) 기법을 적용하여 특징 벡터의 차원을 축소시키는 방법을 제안한다. 차원이 축소된 특징 벡터들에 대해서는 SVM (support vector machine) 기법을 적용하여 차량 색상을 인식하기 위해 사용한다. 특징 벡터의 차원을 1/32로 축소한 결과, 차원이 축소되기 이전의 특징 벡터와 비교하여 약 1.42%의 미소한 차이로 색상 인식 성공률이 감소하였다. 또한, 색상 인식의 수행 시간은 1/31로 단축됨으로써 효율적으로 색상 인식을 수행할 수 있었다.
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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