Color recognition of clothes based on k-means and mean shift
Xingming Zheng, Ningzhong Liu · 2012
According to the problem of color recognition of clothes for image searching on web, a kind of clustering main color detection with one step of k-means and one step of mean shift is adopted and the color of clothes can be detected accurately. It uses the k-means to extract the background and foreground of a picture, which contains the information of a person dressed up with a color cloth in a complex background. The first step aims to recognize the position of the cloth content and lows the influence of the background. After the first step of k-means, we reconstruct the image as the clustering data for the second step of mean shift. The recognition effect achieves above 85% by calculating the distance of the clustering center of our color model of pixel, comparing to the database with the accurately color map of our clothes' images. The experiment of our approach shows that the two-steps k-means and mean shift of color recognition is improved. We developed a color recognition system of clothes for the image searching using that approach and the recognition performance is optimized.