Fast Image Segmentation Based on Adaptive Histogram Thresholding

In this paper, a new method for color image segmentation is presented. This method is based on histogram thresholding and correlation between the difference of color components. Hence, nearly all histogram thresholding methods work only in one or two dimensions of gray scale histogram, neighborhood, probability function or entropy. The proposed method will try to use color components as the main features of segmentation by finding the correlation between the peaks of histogram in each color component. It will help us to find main color components of each object and the background of image. While, we have main color components; it will be easy to use parallel processing to segment entire image at once without using any neighborhood window or losing any data in color space transform into gray scale. With these benefits, a fast and accurate method based on adaptive histogram thresholding is presented in this paper for segmentation of color images. The experimental results on benchmark datasets demonstrate the efficiency of the proposed method.