A Context Sensitive Multilevel Thresholding Using Swarm Based Algorithms

S. Pare

doi:10.1109/JAS.2017.7510697

A Context Sensitive Multilevel Thresholding Using Swarm Based Algorithms

2019-11-01

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DOI：10.1109/JAS.2017.7510697

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In this paper, a comprehensive energy function is used to formulate the three most popular objective functions: Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur's entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.

Keyword:

Color image segmentation

Kapur's entropy

multilevel thresholding

Otsu method

swarm based optimization algorithms

Tsalli's entropy

A Context Sensitive Multilevel Thresholding Using Swarm Based Algorithms

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