Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy
The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expe...
| 主要な著者: | , , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Elsevier
2014
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| 主題: | |
| オンライン･アクセス: | http://eprints.um.edu.my/10614/1/00012993_97976.pdf |
| _version_ | 1825719887812624384 |
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| author | Bhandari, A.K. Singh, V.K, Kumar, A. Singh, G.K. |
| author_facet | Bhandari, A.K. Singh, V.K, Kumar, A. Singh, G.K. |
| author_sort | Bhandari, A.K. |
| collection | UM |
| description | The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects
the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel
thresholding using Kapur’s entropy has been employed. For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur’s entropy for optimal multilevel thresholding. A new approach of CS and WDO algorithm is used for selection of optimal threshold value. This algorithm is used to obtain the best solution or best fitness value from the initial random threshold values, and to evaluate the quality of a solution, correlation function is used. Experimental results have been examined on standard set of satellite images using various numbers of thresholds. The results based on Kapur’s
entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem. |
| first_indexed | 2024-03-06T05:26:35Z |
| format | Article |
| id | um.eprints-10614 |
| institution | Universiti Malaya |
| language | English |
| last_indexed | 2024-03-06T05:26:35Z |
| publishDate | 2014 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | um.eprints-106142018-10-01T03:49:27Z http://eprints.um.edu.my/10614/ Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy Bhandari, A.K. Singh, V.K, Kumar, A. Singh, G.K. TK Electrical engineering. Electronics Nuclear engineering The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur’s entropy has been employed. For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur’s entropy for optimal multilevel thresholding. A new approach of CS and WDO algorithm is used for selection of optimal threshold value. This algorithm is used to obtain the best solution or best fitness value from the initial random threshold values, and to evaluate the quality of a solution, correlation function is used. Experimental results have been examined on standard set of satellite images using various numbers of thresholds. The results based on Kapur’s entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem. Elsevier 2014 Article PeerReviewed application/pdf en http://eprints.um.edu.my/10614/1/00012993_97976.pdf Bhandari, A.K. and Singh, V.K, and Kumar, A. and Singh, G.K. (2014) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Systems with Applications (41). pp. 3538-3560. ISSN 0957-4174, |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Bhandari, A.K. Singh, V.K, Kumar, A. Singh, G.K. Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy |
| title | Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy |
| title_full | Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy |
| title_fullStr | Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy |
| title_full_unstemmed | Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy |
| title_short | Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy |
| title_sort | cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using kapur s entropy |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.um.edu.my/10614/1/00012993_97976.pdf |
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