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Entropy 2011, 13(4), 841-859; doi:10.3390/e13040841
Article

Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach

 and
*
School of Information Science and Engineering, Southeast University, Nanjing 210096, China
* Author to whom correspondence should be addressed.
Received: 2 March 2011 / Revised: 17 March 2011 / Accepted: 29 March 2011 / Published: 13 April 2011
(This article belongs to the Special Issue Tsallis Entropy)
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Abstract

This paper proposes a global multi-level thresholding method for image segmentation. As a criterion for this, the traditional method uses the Shannon entropy, originated from information theory, considering the gray level image histogram as a probability distribution, while we applied the Tsallis entropy as a general information theory entropy formalism. For the algorithm, we used the artificial bee colony approach since execution of an exhaustive algorithm would be too time-consuming. The experiments demonstrate that: 1) the Tsallis entropy is superior to traditional maximum entropy thresholding, maximum between class variance thresholding, and minimum cross entropy thresholding; 2) the artificial bee colony is more rapid than either genetic algorithm or particle swarm optimization. Therefore, our approach is effective and rapid.
Keywords: image segmentation; multi-level thresholding; maximum Tsallis entropy; artificial bee colony image segmentation; multi-level thresholding; maximum Tsallis entropy; artificial bee colony
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Zhang, Y.; Wu, L. Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach. Entropy 2011, 13, 841-859.

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