Paper Info
Title
Image segmentation using multilevel thresholding based on modified bird mating optimization
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.
Year
DOI
Venue
2019
10.1007/s11042-019-7515-6
Multimedia Tools and Applications
Keywords
Field
DocType
Image segmentation, Multilevel thresholding, Bird mating optimization, Differential evolutionary
Particle swarm optimization,Computer vision,Hybrid algorithm,Pattern recognition,Segmentation,Computer science,Image segmentation,Gray level,Optimization algorithm,Artificial intelligence,Thresholding,Genetic algorithm
Journal
Volume
Issue
ISSN
78
16
1380-7501
Citations 
PageRank 
References 
5
0.45
0
Authors
5
Name
Order
Citations
PageRank
Maliheh Ahmadi150.45
Kamran Kazemi28112.24
Ardalan Aarabi3222.60
Taher Niknam4201.81
Mohammad Sadegh Helfroush57011.30