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.

中文翻译：

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基于改进果蝇优化算法的织物图案分割

为了提高印花图案的分割性能，设计了基于3D最大熵的分割准则，该准则通过改进的果蝇优化算法进行了优化。三元组由像素的灰度值，对角线的平均灰度值和附近的非对角线像素组成。根据三元组的联合概率，可以设计物体和背景区域的3D熵。最佳分割阈值可通过最大化3D熵来解决。设计了一种混合果蝇优化算法来优化3D熵函数。利用混沌搜索来增强果蝇搜索的遍历性，并引入拥挤程度来增强整体搜索能力。实验结果表明，基于3D熵最大化的分割方法可以提高印花图案的分割性能，并且可以很好地保留图案信息。改进后的果蝇算法具有更高的优化效率，并且优化时间可以减少到原始算法的30％。