Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach
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 en...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2011-04-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1099-4300/13/4/841/ |
| _version_ | 1811303293893214208 |
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| author | Yudong Zhang Lenan Wu |
| author_facet | Yudong Zhang Lenan Wu |
| author_sort | Yudong Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T07:45:26Z |
| format | Article |
| id | doaj.art-10effe30a27346d5aefbf06db907c774 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-13T07:45:26Z |
| publishDate | 2011-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-10effe30a27346d5aefbf06db907c7742022-12-22T02:55:42ZengMDPI AGEntropy1099-43002011-04-0113484185910.3390/e13040841Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony ApproachYudong ZhangLenan WuThis 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.http://www.mdpi.com/1099-4300/13/4/841/image segmentationmulti-level thresholdingmaximum Tsallis entropyartificial bee colony |
| spellingShingle | Yudong Zhang Lenan Wu Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach Entropy image segmentation multi-level thresholding maximum Tsallis entropy artificial bee colony |
| title | Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach |
| title_full | Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach |
| title_fullStr | Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach |
| title_full_unstemmed | Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach |
| title_short | Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach |
| title_sort | optimal multi level thresholding based on maximum tsallis entropy via an artificial bee colony approach |
| topic | image segmentation multi-level thresholding maximum Tsallis entropy artificial bee colony |
| url | http://www.mdpi.com/1099-4300/13/4/841/ |
| work_keys_str_mv | AT yudongzhang optimalmultilevelthresholdingbasedonmaximumtsallisentropyviaanartificialbeecolonyapproach AT lenanwu optimalmultilevelthresholdingbasedonmaximumtsallisentropyviaanartificialbeecolonyapproach |