Unsupervised low-frequency driven segmentation of color images

This paper presents an algorithm for unsupervised segmentation of color images. The main idea behind it is the use of the low-frequency content of images which allows for smoothing of segments and sharpening of histograms of color attributes. Our algorithm handles images in a palettized format and operates in the feature space constituted by the cylindrical representation of the L*u*ν* color space. Within such space, it finds representative colors by determining first the main hue families, through histogram thresholding, and then the main clusters on planes at constant hue, by means of k-means clustering. Two examples of the practical performance of the algorithm are reported and discussed