Integrating a Genetic Algorithm and a Self-organizing Map Network for an Automatically Separating Color Printed Fabric System

Abstract

We need more than words and simple methods to describe the various different color patterns found on printed fabrics nowadays. The complexity in pattern identification has made the analysis and comparison difficult and will have to be managed manually. The automatic computer color separating system for printed fabrics, proposed in this paper, integrates a genetic algorithm (GA) and a self-organizing map network (SOMN) to automatically separate printed colors, so as to eliminate the time-consuming manual color segmentation and registration currently done in the industry. The system first uses a color scanner to record RGB color images of the printed fabrics and uses median filter processing to reduce color changes due to uneven light reflections arising from the fabric surface weaving texture. Then RGB color space is transformed to HSI color space so that color analysis can match human color sense and use customary procedures. Next, the GA is employed to search for color distributions that are the same as the original image of printed fabrics. The area of each sub-image is 9.06% of the original image, not only reducing color segmentation operation time, but completely reserving the print structure and color distribution of the original image. Afterwards, color characteristic values are obtained in HSI color space. Finally, SOMN is adopted for the color segmentation operation. According to our experimental results, this system can rapidly and automatically complete color separation and identify repeating patterns in images from printed fabrics.