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Information 2017, 8(1), 16; doi:10.3390/info8010016

A Quick Artificial Bee Colony Algorithm for Image Thresholding

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1
College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2
College of Information Engineering, Fuyang Teachers College, Fuyang 236041, China
3
Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
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Author to whom correspondence should be addressed.
Received: 26 October 2016 / Revised: 20 January 2017 / Accepted: 24 January 2017 / Published: 28 January 2017
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Abstract

The computational complexity grows exponentially for multi-level thresholding (MT) with the increase of the number of thresholds. Taking Kapur’s entropy as the optimized objective function, the paper puts forward the modified quick artificial bee colony algorithm (MQABC), which employs a new distance strategy for neighborhood searches. The experimental results show that MQABC can search out the optimal thresholds efficiently, precisely, and speedily, and the thresholds are very close to the results examined by exhaustive searches. In comparison to the EMO (Electro-Magnetism optimization), which is based on Kapur’s entropy, the classical ABC algorithm, and MDGWO (modified discrete grey wolf optimizer) respectively, the experimental results demonstrate that MQABC has exciting advantages over the latter three in terms of the running time in image thesholding, while maintaining the efficient segmentation quality. View Full-Text
Keywords: image segmentation; swarm based algorithms; multilevel thresholds; image thresholding image segmentation; swarm based algorithms; multilevel thresholds; image thresholding
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, L.; Sun, L.; Guo, J.; Han, C.; Zhou, J.; Li, S. A Quick Artificial Bee Colony Algorithm for Image Thresholding. Information 2017, 8, 16.

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