通过对传统彩色图像分割方法的分析，结合最新的交叉视觉皮质模型，给出了一种新的彩色图像自动分割算法。将图像转换到HSV色彩空间，并利用判决机制选择熵最大的分量进行分割，与传统的对H，S，V分别进行处理并将处理结果合并作为分割结果的方法相比，传统HSV方法耗时7533 s,该算法整个处理过程耗时257 s，约为传统HSV方法耗时的三分之一,大大提高了处理的速度；将最大类间交叉熵判决机制引入到交叉视觉皮质模型中，从而实现图像自动分割，避免了人为干预，提高了分割的准确性。将该方法与基于最大香农熵的分割方法进行了比较，仿真结果表明，该算法对于彩色图像自动分割具有良好的性能。

A new algorithm is proposed to segment color image automatically using the intersecting cortical model with the reference of traditional color image segmentation method. The algorithm converts images into HSV color space and selects one of the H, S, and V components with the decision rule of maximum entropy. And it has increased the processing speed greatly compared with traditional color image segmentation method which deals with the three components individually and then merges the results. Our new algorithm costs 257 s, while about one-third of that of the traditional color image segmentation method uses 7533 s. The automatic segmentation with less artificial sets and high accuracy is realized by introducing the maximum cross entropy decision rule into the intersecting cortical model. The new algorithm was compared to the image segmentation method based on max entropy. And the simulation results show that the new algorithm has good performance in color image automatic segmentation.