SHAPE RECOGNITION USING ARTIFICIAL BEE COLONY OPTIMIZATION

Abstract

The edge potential function (EPF) approach is a promising edge-based shape matching tool for visual target
recognition, and describes the similarity between contours by means of a potential field. However, background noise in
test images may degrade the accuracy of the EPF approach in the identification of target contours. Furthermore, the
computational load of the EPF approach is usually heavy, thus limiting its use in online applications. To solve these
problems, this paper proposes a new shape matching tool based on atomic potential function (APF). The APF approach
reduces the effects of background noise by introducing the concept of atom potential to the generation of potential fields.
Moreover, in our proposed APF approach, the potential field is calculated using the contour extracted from a predefined target template rather than contours extracted from test images. Following the calculation of the potential field,
the derived potential field is transformed to match the contours extracted from the test images. The search process for the
transformation that matches the contours most closely is modeled as an optimization problem solved by a modified
version of the artificial bee colony (ABC) algorithm – the internal feedback ABC (IF-ABC). Compared to the
conventional ABC algorithm, IF-ABC effectively avoids premature convergence and significantly improves convergence
speed. Experimental results verify the feasibility and efficiency of our proposed APF approach by comparing it with the
traditional EPF method.