Teaching-learning-based optimization algorithm to minimize cross entropy for Selecting multilevel threshold values
Image thresholding is one of the most important approaches for image segmentation. Among multilevel thresholding techniques, cross entropy has been widely used by researchers to find multilevel threshold values. In multilevel cross entropy thresholding techniques, main target is to find an optimal c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2019-03-01
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| Series: | Egyptian Informatics Journal |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866517302736 |
| _version_ | 1818889954786279424 |
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| author | Harmandeep Singh Gill Baljit Singh Khehra Arjan Singh Lovepreet Kaur |
| author_facet | Harmandeep Singh Gill Baljit Singh Khehra Arjan Singh Lovepreet Kaur |
| author_sort | Harmandeep Singh Gill |
| collection | DOAJ |
| description | Image thresholding is one of the most important approaches for image segmentation. Among multilevel thresholding techniques, cross entropy has been widely used by researchers to find multilevel threshold values. In multilevel cross entropy thresholding techniques, main target is to find an optimal combination of threshold values at different levels for minimizing the cross entropy. In this paper, Teaching-Learning-based Optimization (TLBO) algorithm is used to find an optimal combination of threshold values at different levels for minimizing the cross entropy. TLBO algorithm is inspired by passing on knowledge within a classroom environment where students first gain knowledge from a teacher and then through mutual interaction. This new proposed approach is called the TLBO-based minimum cross entropy thresholding (TLBO-based MCET) algorithm. The performance of the proposed algorithm is tested on a number of standard test images. For comparative analysis, the results of TLBO-based MCET algorithm are compared with the results of Firefly-based minimum cross entropy thresholding (FF-based MCET), Honey Bee Mating Optimization-based minimum cross entropy thresholding (HBMO-based MCET) and Quantum Particle Swarm Optimization-based minimum cross entropy thresholding (Quantam PSO-based MCET). Comparative analysis is done based on two most popular objective performance measures, Peak Signal to Noise Ratio (PSNR) and Uniformity. From experimental results, it is observed that the proposed method is an efficient and feasible method to search an optimal combination of threshold values at 2nd, 3rd, 4th and 5th levels. Keywords: Cross entropy, Teacher-Learning-based Optimization (TLBO), Thresholding, PSNR, Uniformity |
| first_indexed | 2024-12-19T17:17:14Z |
| format | Article |
| id | doaj.art-10e6500376e4423aa0f1525116a3149f |
| institution | Directory Open Access Journal |
| issn | 1110-8665 |
| language | English |
| last_indexed | 2024-12-19T17:17:14Z |
| publishDate | 2019-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj.art-10e6500376e4423aa0f1525116a3149f2022-12-21T20:12:50ZengElsevierEgyptian Informatics Journal1110-86652019-03-012011125Teaching-learning-based optimization algorithm to minimize cross entropy for Selecting multilevel threshold valuesHarmandeep Singh Gill0Baljit Singh Khehra1Arjan Singh2Lovepreet Kaur3Department of Computer Applications, I. K. Gujral Punjab Technical University, Jalandhar-144603, Punjab, India; Corresponding author.Department of Computer Science & Engineering, Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib-140407, Punjab, IndiaDepartment of Mathematics, Punjabi University, Patiala-147002, Punjab, IndiaDepartment of Mathematics, Punjabi University, Patiala-147002, Punjab, IndiaImage thresholding is one of the most important approaches for image segmentation. Among multilevel thresholding techniques, cross entropy has been widely used by researchers to find multilevel threshold values. In multilevel cross entropy thresholding techniques, main target is to find an optimal combination of threshold values at different levels for minimizing the cross entropy. In this paper, Teaching-Learning-based Optimization (TLBO) algorithm is used to find an optimal combination of threshold values at different levels for minimizing the cross entropy. TLBO algorithm is inspired by passing on knowledge within a classroom environment where students first gain knowledge from a teacher and then through mutual interaction. This new proposed approach is called the TLBO-based minimum cross entropy thresholding (TLBO-based MCET) algorithm. The performance of the proposed algorithm is tested on a number of standard test images. For comparative analysis, the results of TLBO-based MCET algorithm are compared with the results of Firefly-based minimum cross entropy thresholding (FF-based MCET), Honey Bee Mating Optimization-based minimum cross entropy thresholding (HBMO-based MCET) and Quantum Particle Swarm Optimization-based minimum cross entropy thresholding (Quantam PSO-based MCET). Comparative analysis is done based on two most popular objective performance measures, Peak Signal to Noise Ratio (PSNR) and Uniformity. From experimental results, it is observed that the proposed method is an efficient and feasible method to search an optimal combination of threshold values at 2nd, 3rd, 4th and 5th levels. Keywords: Cross entropy, Teacher-Learning-based Optimization (TLBO), Thresholding, PSNR, Uniformityhttp://www.sciencedirect.com/science/article/pii/S1110866517302736 |
| spellingShingle | Harmandeep Singh Gill Baljit Singh Khehra Arjan Singh Lovepreet Kaur Teaching-learning-based optimization algorithm to minimize cross entropy for Selecting multilevel threshold values Egyptian Informatics Journal |
| title | Teaching-learning-based optimization algorithm to minimize cross entropy for Selecting multilevel threshold values |
| title_full | Teaching-learning-based optimization algorithm to minimize cross entropy for Selecting multilevel threshold values |
| title_fullStr | Teaching-learning-based optimization algorithm to minimize cross entropy for Selecting multilevel threshold values |
| title_full_unstemmed | Teaching-learning-based optimization algorithm to minimize cross entropy for Selecting multilevel threshold values |
| title_short | Teaching-learning-based optimization algorithm to minimize cross entropy for Selecting multilevel threshold values |
| title_sort | teaching learning based optimization algorithm to minimize cross entropy for selecting multilevel threshold values |
| url | http://www.sciencedirect.com/science/article/pii/S1110866517302736 |
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