Color segmentation in multicolor images using node-growing self-organizing map

This article presents a clustering algorithm based on node-growing self-organizing map (NGSOM) to classify colors on color images automatically and accurately partition the regions of different colors for color measurement. Based on the CIEDE2000 criterion, pixels in a multicolor image are grouped into a number of visually distinguishable color regions in which pixel distribution information is provided as the input of the NGSOM network for further segmentation. As an unsupervised clustering algorithm, the NGSOM randomly selects two initial nodes from the input source without a predefined network structure and grows nodes according to color differences between the first best matching unit (FBMU) and the second best matching unit (SBMU) of the current input data. Unlike a traditional SOM, the NGSOM utilizes learning rates varying with the pixel distributions of major color regions. The node-growing procedure is terminated when all the input data are examined. Compared with some commonly used color clustering algorithms, the proposed algorithm possesses a better peak signal-to-noise ratio (PSNR) and higher time efficiency. The NGSOM can be used for a wide range of applications, including fabric colorfastness assessment, painting conservation, and scenic identification in aerial images.