A PSO-Based Method for Min-ε Approximation of Closed Contour Curves

Authors:
Bin Wang;Chaojian Shi;Jing Li
Affiliations:
Key Laboratory of Electronic Business, Nanjing University of Finance and Economics, Nanjing, P.R. China 210003;Merchant Marine College, Shanghai Maritime University, Shanghai, P.R. China 200135 and Department of Computer Science and Engineering, Fudan University, Shanghai, P.R. China 200433;Alcatel Shanghai Bell Company Limited, Shanghai, P.R. China 201206
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

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Abstract

Finding a polygon to approximate the contour curve with theminimal approximation error εunder thepre-specified number of vertices, is termed min-εproblem. It is an important issue in image analysis and patternrecognition. A discrete version of particle swarm optimization(PSO) algorithm is proposed to solve this problem. In this method,the position of each particle is represented as a binary stringwhich corresponds to an approximating polygon. Many particles forma swarm to fly through the solution space to seek the best one. Forthose particles which fly out of the feasible region, thetraditional split and merge techniques are applied to adjust theirposition which can not only move the particles from the infeasiblesolution space to the feasible region, but also relocate it in abetter site. The experimental results show that the proposedPSO-based method has the higher performance over the GA-basedmethods.