Segmentation of the Fabric Pattern Based on Improved Fruit Fly Optimization Algorithm

Abstract

In order to improve the segmentation performance of the printed fabric pattern, a segmentation criterion based on the 3D maximum entropy which is optimized by an improved fruit fly optimization algorithm is designed. The triple is composed of the gray value of the pixel, the average gray values of the diagonal, and the nondiagonal pixels in the neighbourhood. According to the joint probability of the triple, the 3D entropy of the object and the background areas could be designed. The optimal segmentation threshold is resolved by maximizing the 3D entropy. A hybrid fruit fly optimization algorithm is designed to optimize the 3D entropy function. Chaos search is used to enhance the ergodicity of the fruit fly search, and the crowding degree is introduced to enhance the global searching ability. Experiment results show that the segmentation method based on maximizing the 3D entropy could improve the segmentation performance of the printed fabric pattern and the pattern information could be reserved well. The improved fruit fly algorithm has a higher optimization efficiency, and the optimization time could be reduced to 30 percent of the original algorithm.