Image Segmentation Based on Learning Cellular Automata Using Soft Computing Approach

Image Segmentation refers to the process of partitioning a digital image into multiple segments. The goal of segmentation is to simplify and change the representation of an image into something that is more meaningful and easier to analyze. A Cellular Automata (CA) is a computing model of complex system using simple rule. It divides the problem space into number of cells and each cell can be in one or several final states. Cells are affected by its neighbor's to the simple rule. Learning Cellular Automata (LCA) is a variant of automata that interact with random environment having as goal to improve its behavior. This paper proposes an image segmentation technique based on LCA using soft computing approach. This proposed method works in two steps, the first step is called as soft segmentation where the input image(s) is/are analyzed through LCA and the second step is called as soft computing approach where the analyzed image is segmented through fuzzy C-means algorithm.