Title: Performance evaluation of Shannon and non-Shannon fuzzy 2-partition entropies for image segmentation using teaching-learning-based optimisation
Authors: Baljit Singh Khehra; Arjan Singh; Gurdeep Singh Hura
Addresses: Department of Computer Science and Engineering, Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib-140407, Punjab, India ' Department of Mathematics, Punjabi University, Patiala-147002, Punjab, India ' Department of Mathematics and Computer Science, University of Maryland Eastern Shore, Princess Anne, Maryland, USA
Abstract: Thresholding has been used extensively by different researchers for the segmentation of images due to its accuracy and precision. Fuzzy 2-partition entropy with various evolutionary algorithms including teaching-learning-based optimisation (TLBO) has been used widely to determine optimal threshold value for image segmentation. Fuzzy 2-partition Shannon entropy is generally applied for thresholding. In this paper, Havrda-Charvat fuzzy 2-partition entropy and Renyi fuzzy 2-partion entropy-based TLBO techniques have been proposed for selecting optimal threshold value. The performance of fuzzy 2-partition Shannon and non-Shannon measures of entropy using TLBO has been compared with other nature-based evolutionary algorithms namely genetic algorithm, biogeography-based optimisation and with a recursive approach, which is a non-evolutionary approach. From the results, it has been observed that TLBO-based Havrda-Charvat fuzzy 2-partition entropy gives better performance than all other approaches in terms of quality of the segmented image as well as taking less computational time.
Keywords: Shannon entropy; Havrda-Charvat entropy; Renyi entropy; Kapur entropy; teaching-learning-based optimisation; TLBO; GA; biogeography-based optimisation; BBO.
International Journal of Computational Vision and Robotics, 2022 Vol.12 No.3, pp.250 - 298
Received: 24 Mar 2020
Accepted: 17 Apr 2021
Published online: 11 Mar 2022 *