An image segmentation approach based on chaotic ant colony algorithms

摘要

Image segmentation is to partition an image into meaningful regions. An image segmentation approach based on chaotic ant colony algorithm is presented in this paper. The approach performs the image segmentation by selecting the optimal threshold values, where the multi-threshold values are used. First of all, an entropy function corresponding to an image is defined. The optimal threshold values are obtained by making the entropy function reach the maximal value. Secondly, an approach based on ant colony algorithm is presented for the computation of the optimal thresholds. In order to improve the computation performance of ant colony algorithms, for example, to avoid the algorithm search being trapped in local optimum, we use chaotic approach to find a better solution whenever all the ants have finished the operations. The chaotic approach searching the space around the ant which is the best so far. Besides, the initial solutions are generated by chaotic approach, this improves the quality of initial ants. The experimental results show that the approach proposed in this paper can get the near optimal threshold.