A fuzzy partition entropy approach for multi-thresholding segmentation based on the recursive artificial bee colony algorithm

作者：Yin Shibai; Zhao Xiangmo^*; Wang Weixing; Wang Yibin

来源：Journal of Xi'an Jiaotong University, 2012, 46(10): 72-77.

• Artificial bee colonies
• Artificial bee colony algorithms
• Entropy algorithms
• Entropy approach
• Exhaustive search
• Fitness values
• Fuzzy entropy
• Fuzzy partition
• Multilevel thresholding
• Multiple threshold
• Optimal threshold
• Recursive process
• Recursive programming
• Runtimes

A new recursive artificial bee colony fuzzy partition entropy algorithm (RAFPEA) for multi-thresholding image segmentation is proposed to solve the inefficiency and repeated computation in fuzzy partition entropy approach for selecting the thresholds in the process of image segmentation. The membership functions with attached boundary conditions and gray weights are selected to build the image fuzzy entropy model. The combined computation of different variables in this model is converted to the recursive process and the no-repetitive results of the processing moments are stored. Then the artificial bee colony algorithm (ABCA) uses the stored results to calculate the fitness value of individual species in the ABCA so that the repeated calculations can be reduced and the optimal thresholds can be searched effectively. Experimental results and comparisons with common algorithms indicate that the run time accounts for 5% of ones of the fuzzy partition entropy approaches based on exhaustive algorithm and genetic algorithm. And the uniformity obtained by the proposed scheme is equivalent to the one via exhaustive search. Moreover, as the number of required thresholds increases, the run time keeps stable. The RAFPEA can effectively segment images by multiple thresholds with ensured precision.

• 出版日期2012
• 单位西北工业大学