Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation

研究成果: Article同行評審

253 引文 斯高帕斯(Scopus)

摘要

Multilevel thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding (MET) has been widely applied in the literature. In this paper, a new multilevel MET algorithm based on the technology of the artificial bee colony (ABC) algorithm is proposed: the maximum entropy based artificial bee colony thresholding (MEABCT) method. Four different methods are compared to this proposed method: the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO), the Fast Otsu's method and the honey bee mating optimization (HBMO). The experimental results demonstrate that the proposed MEABCT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. Compared to the other four thresholding methods, the segmentation results of using the MEABCT algorithm is the most, however, the computation time by using the MEABCT algorithm is shorter than that of the other four methods.

原文English
頁(從 - 到)13785-13791
頁數7
期刊Expert Systems With Applications
38
發行號11
DOIs
出版狀態Published - 2011 10月

All Science Journal Classification (ASJC) codes

  • 一般工程
  • 電腦科學應用
  • 人工智慧

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