Research Area:  Metaheuristic Computing

Abstract:

Multilevel thresholding using Otsu or Kapur methods is widely used in the context of image segmentation. These methods select optimal thresholds in gray level images by maximizing between-class variance or entropy criterion. These methods become time consuming and less efficient with increasing number of thresholds. To increase the efficiency of the image segmentation using multilevel thresholding based on Kapur and Otsu methods, we developed a hybrid optimization algorithm named BMO-DE based on bird mating optimization (BMO) and differential evolutionary (DE) algorithms. The efficiency of the proposed method was evaluated on eight standard benchmark images. The proposed method achieved better segmentation results in term of solution quality and stability in comparison with other well-known techniques including bacterial foraging (BF), modified bacterial foraging (MBF), particle swarm optimization (PSO), genetic algorithm (GA) and hybrid algorithm named PSO-DE.

Keywords:  
Multilevel thresholding
image segmentation
entropy criterion
differential evolutionary algorithm
bacterial foraging
particle swarm optimization
genetic algorithm

Author(s) Name:  Maliheh Ahmadi, Kamran Kazemi, Ardalan Aarabi, Taher Niknam & Mohammad Sadegh Helfroush

Publisher name:  Springer

DOI:  10.1007/s11042-019-7515-6

Volume Information:  78, pages 23003–23027

Paper Link:   https://link.springer.com/article/10.1007/s11042-019-7515-6