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 expen...
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| Published in | Expert systems with applications Vol. 41; no. 7; pp. 3538 - 3560 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier Ltd
01.06.2014
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0957-4174 1873-6793 |
| DOI | 10.1016/j.eswa.2013.10.059 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Singh, Girish Kumar Singh, Vineet Kumar Bhandari, Ashish Kumar Kumar, Anil |
| Author_xml | – sequence: 1 givenname: Ashish Kumar surname: Bhandari fullname: Bhandari, Ashish Kumar email: bhandari.iiitj@gmail.com organization: Indian Institute of Information Technology Design and Manufacturing, Jabalpur 482011, Madhya Pradesh, India – sequence: 2 givenname: Vineet Kumar surname: Singh fullname: Singh, Vineet Kumar email: viiitdmj@gmail.com organization: Indian Institute of Information Technology Design and Manufacturing, Jabalpur 482011, Madhya Pradesh, India – sequence: 3 givenname: Anil surname: Kumar fullname: Kumar, Anil email: anilkdee@gmail.com organization: Indian Institute of Information Technology Design and Manufacturing, Jabalpur 482011, Madhya Pradesh, India – sequence: 4 givenname: Girish Kumar surname: Singh fullname: Singh, Girish Kumar email: gksngfee@gmail.com organization: Department of Electrical Engineering, Indian Institute Technology Roorkee, Uttrakhand 247667, India |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28319065$$DView record in Pascal Francis |
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| Keywords | Kapur’s entropy Image segmentation Multilevel thresholding Wind driven optimization Swarm intelligence Cuckoo search algorithm Particle swarm optimization Artificial life Image processing Threshold detection Entropy Correlation function Threshold function Threshold Probabilistic approach Initial value problem Space remote sensing Parasite Global optimum Standards Search algorithm Experimental result Kapur's entropy Quality control Heuristic method Objective function Artificial intelligence |
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| SubjectTerms | Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Artificial intelligence Computer science; control theory; systems Cuckoo search algorithm Earth, ocean, space Entropy Exact sciences and technology External geophysics Image segmentation Kapur’s entropy Mathematical analysis Mathematical models Mathematical programming Multilevel Multilevel thresholding Operational research and scientific management Operational research. Management science Optimization Particle swarm optimization Pattern recognition. Digital image processing. Computational geometry Remote sensing, photointerpretation Satellite imagery Searching Swarm intelligence Theoretical computing Thresholds Wind driven optimization |
| Title | Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy |
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