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

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Received: 2 March 2011; in revised form: 17 March 2011 / Accepted: 29 March 2011 / Published: 13 April 2011
(This article belongs to the Special Issue Tsallis Entropy)
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.
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
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MDPI and ACS Style

Zhang, Y.; Wu, L. Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach. Entropy 2011, 13, 841-859.

AMA Style

Zhang Y, Wu L. Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach. Entropy. 2011; 13(4):841-859.

Chicago/Turabian Style

Zhang, Yudong; Wu, Lenan. 2011. "Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach." Entropy 13, no. 4: 841-859.

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