An evolutionary autonomous agents approach to image feature extraction

This paper presents a new approach to image feature extraction which utilizes evolutionary autonomous agents. Image features are often mathematically defined in terms of the gray-level intensity at image pixels. The optimality of image feature extraction is to find all the feature pixels from the image. In the proposed approach, the autonomous agents, being distributed computational entities, operate directly in the 2-D lattice of a digital image and exhibit a number of reactive behaviors. To effectively locate the feature pixels, individual agents sense the local stimuli from their image environment by means of evaluating the gray-level intensity of locally connected pixels, and accordingly activate their behaviors. The behavioral repository of the agents consists of: 1) feature-marking at local pixels and self-reproduction of offspring agents in the neighboring regions if the local stimuli are found to satisfy feature conditions, 2) diffusion to adjacent image regions if the feature conditions are not held, or 3) death if the agents exceed their life span. As part of the behavior evolution, the directions in which the agents self-reproduce and/or diffuse are inherited from the directions of their selected high-fitness parents. Here the fitness of a parent agent is defined according to the steps that the agent takes to locate an image feature pixel.