Color segmentation of multi-colored fabrics using self-organizing-map based clustering algorithm

Fabric prints may contain intricate and nesting color patterns. To evaluate colors on such a fabric, regions of different colors must be measured individually. Therefore, precise separation of colored patterns is paramount in analyzing fabric colors for digital printing, and in assessing the colorfastness of a printed fabric after a laundering or abrasion process. This paper presents a self-organizing-map (SOM) based clustering algorithm used to automatically classify colors on printed fabrics and to accurately partition the regions of different colors for color measurement. The main color categories of an image are firstly identified and flagged using the SOM's density map and U-matrix. Then, the region of each color category is located by divining the U-matrix map with an adaptive threshold, which is determined by recursively decreasing it from a high threshold until all the flagged neurons are assigned to different regions in the divided map. Finally, the regions with high color similarity are merged to avoid possible over-segmentation. Unlike many other clustering algorithms, this algorithm does not need to pre-define the number of clusters (e.g. main colors) and can automatically select a distance threshold to partition the U-matrix map. The experimental results show that the intricate color patterns can be precisely separated into individual regions representing different colors.