A fast recursive algorithm based on fuzzy 2-partition entropy approach for threshold selection

Authors:
Yinggan Tang;Weiwei Mu;Ying Zhang;Xuguang Zhang
Affiliations:
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China and Institute of Electrical Engineering, Yanshan University, ...;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China;National Lighting Test Center, Beijing 100022, China;Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
Venue:
Neurocomputing
Year:
2011

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Abstract

The fuzzy c-partition entropy approach for threshold selection is an effective approach for image segmentation. The approach models the image with a fuzzy c-partition, which is obtained using parameterized membership functions. The ideal threshold is determined by searching an optimal parameter combination of the membership functions such that the entropy of the fuzzy c-partition is maximized. It involves large computation when the number of parameters needed to determine the membership function increases. In this paper, a recursive algorithm is proposed for fuzzy 2-partition entropy method, where the membership function is selected as S-function and Z-function with three parameters. The proposed recursive algorithm eliminates many repeated computations, thereby reducing the computation complexity significantly. The proposed method is tested using several real images, and its processing time is compared with those of basic exhaustive algorithm, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO) and simulated annealing (SA). Experimental results show that the proposed method is more effective than basic exhaustive search algorithm, GA, PSO, ACO and SA.