A Global Multilevel Thresholding Using Differential Evolution Approach

Abstract EN

Otsu’s function measures the properness of threshold values in multilevel image thresholding.

Optimal threshold values are necessary for some applications and a global search algorithm is required.

Differential evolution (DE) is an algorithm that has been used successfully for solving this problem.

Because the difficulty of a problem grows exponentially when the number of thresholds increases, the ordinary DE fails when the number of thresholds is greater than 12.

An improved DE, using a new mutation strategy, is proposed to overcome this problem.

Experiments were conducted on 20 real images and the number of thresholds varied from 2 to 16.

Existing global optimization algorithms were compared with the proposed algorithms, that is, DE, rank-DE, artificial bee colony (ABC), particle swarm optimization (PSO), DPSO, and FODPSO.

The experimental results show that the proposed algorithm not only achieves a more successful rate but also yields a lower threshold value distortion than its competitors in the search for optimal threshold values, especially when the number of thresholds is large.