Color segmentation using UCS perceptual attributes

Image segmentation is an essential and important task for computer vision and pattern recognition. In this paper a general approach for color image segmentation using uniform-chromaticity-scale perceptual color attributes is proposed. First, chromatic and achromatic areas in a perceptual IHS color space are defined. Then, an image is split into chromatic and achromatic regions according to the distribution of the image pixels in the color space. 1-D histogram thresholding for each color attribute is performed to segment the chromatic and achromatic regions. Lastly, a region-growing technique is used to solve the oversegmentation problem. In experiment, we demonstrate the power of the proposed approach; we derive algorithms from the general approach to segment a flower image, to extract a shade-road from scene images of an autonomous land vehicle, to extract color-based information from color documents, etc. The approach can overcome the shade problem in images.