Color recognition with compact color features

For color images, color histograms are generally used as the color feature vectors for classifying the colors of objects. To achieve a higher success rate in color classification, feature vectors with a higher dimension are required, yet this causes a low efficiency with regard to the computation time and memory usage. Therefore, this paper proposes a method of reducing the feature vector dimension by a factor of 170 based on combining two techniques: (i) projecting a color histogram generated in 3D color space into 2D color planes and (ii) converting the color histograms to class histograms using a naive Bayesian classifier. The resulting feature vectors are then classified using a support vector machine method and template matching method to recognize the object colors. With both classification methods, a better recognition rate is achieved than when using the original large feature vectors.