A novel stochastic search method for polygonal approximation problem

Authors:
Bin Wang;Huazhong Shu;Chaojian Shi;Limin Luo
Affiliations:
School of Computer Science and Engineering, Southeast University, Nanjing 210096, China;School of Computer Science and Engineering, Southeast University, Nanjing 210096, China;Department of Computer Science and Engineering, Fudan University, Shanghai 200433, China and Merchant Marine College, Shanghai Maritime University, Shanghai 200135, China;School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
Venue:
Neurocomputing
Year:
2008

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Abstract

A novel stochastic search method (NSSM) is proposed for the polygonal approximation problem. NSSM incorporates the ranking selection scheme, which is initially developed for solving the premature convergence of genetic algorithms (GAs), into the traditional split-and-merge technique. For avoiding getting trapped in a local optimum, NSSM randomly selects the splitting points and the merging points and determines the selection probability using the ranking selection scheme. Three groups of digital curves, including the synthesized benchmark curves and the real image curves, are used to test the performance of NSSM. The experimental results show the higher performance over the other methods including the GA-based methods and the local search methods.