In this paper, a novel methodology is presented to settle the region of interest (ROI) detection problem in vehicle color recognition so as to remove the redundant components of vehicles that interfere greatly with color recognition. In order to make full use of the local color and spatial information, vehicle images are divided into different superpixels at first. The spatial relationship between superpixels and the outermost pixels is then used for the background removal of vehicle images. By comparing with the vehicle window clustering centroids obtained by k-means, the superpixels close to the universal color characteristics of windows are removed so that the dominant color superpixels are determined. Finally, a linear Support Vector Machine classifier is trained for color recognition. The experiments demonstrate that the proposed methodology is effective for color region of interest detection and thus contribute to vehicle color recognition.