Performance evaluation of Shannon and non-Shannon fuzzy 2-partition entropies for image segmentation using teaching-learning-based optimisation
by Baljit Singh Khehra; Arjan Singh; Gurdeep Singh Hura
International Journal of Computational Vision and Robotics (IJCVR), Vol. 12, No. 3, 2022

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.

Online publication date: Tue, 03-May-2022

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