Threshold segmentation using cultural algorithms for image analysis

The image segmentation is often an important step in the analysis of images. In this paper, an image segmentation method based on cultural algorithms is presented. The method performs the image segmentation by selecting the optimal threshold values. 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 algorithm based on the principle of cultural algorithms is presented for the computation of the optimal thresholds. The algorithm consists of three major components: a population space, a belief space, and a communication protocol that describes how knowledge is exchanged between the first two components. The designs and implementations of the three components are given in detail. The experimental results show that the segmentation method proposed in this paper can obtain the near optimal threshold for image segmentation.