“School of Cognitive Sciences”
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| Paper IPM / Cognitive Sciences / 8072 |
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| Abstract: | |||||
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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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