Research on the application of local binary patterns based on color distance in image classification

Abstract

Local Binary Patterns (LBP) is an texture feature, which is widely used in texture discrimination, face recognition, painting classification and other fields. The previous LBP feature extraction methods and many improved algorithms are all based on the gray scale image. Because there is information loss during the conversion from color image to gray image, the LBP methods need combine with other color features to improve the accuracy of classification. In this paper, a LBP encoding method for color image based on color space distance is proposed, which can not only directly achieve LBP feature from color image, but also can be applied to improve various previous methods. The experiment shows that color-based LBP method can filter background information better. With three different classifiers, the accuracy of classification which only used color-based LBP features is about 15% higher than that of existing LBP features. Finally, the application effect in local binary patterns histograms (LBPH) with color-based LBP further proves the advantage.