Two-Dimensional Maximum Entropy Segmentation Based on Ant Colony Optimization
基于蚁群算法的二维最大熵分割算法

The 2-D maximum entropy method reflects information of the gray distribution and space-related information of the neighborhood. Therefore the segmentation result is more accurate than the 1-D method. However its computational cost is an obstacle in application. Ant Colony Optimization is has been successfully applied to some discrete problems, such as the traveling salesman problem. The ant colony optimization is introduced and the 2-D maximum entropy segmentation is presented based on ant colony optimization. Through the experiments of segmenting infrared images, it is about 60 times faster than the traditional exhaustive search algorithm. The proposed algorithm has been proved to be fast, simple and effective.