Hierarchical Image Segmentation Using Ant Colony and Chemical Computing Approach

This paper presents a new method for hierarchical image segmentation. The hierarchical structure is represented by a binary tree with the main image as its root. At the lower levels, each node stands as one image segment, which is described by a weighted graph and may be divided into two new segments at the next level through a specific cut. Graph bi-sectioning is done by the self organizing property of ant systems. Ants are free to wander over one image segment graph to find the best cut on it. When an ant finds a suitable cut, it returns to its colony and leaves a proper value of pheromone over its trail to attract other ants to that cut. By using the Chemical Computing approach in this paper, it is assumed the mobile hormones (pheromone) are secreted which can diffuse around initial positions and attract more ants to the found cut. The advantages of this assumption are reducing the noise effects and improving the convergence speed of ants to find a new selected image segment, which can be seen in the practical results.

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