MMFO: modified moth flame optimization algorithm for region based RGB color image segmentation

• Published in: International journal of electrical and computer engineering (Malacca, Malacca) Vol. 10; no. 1; p. 196
• Main Authors: Jaiswal, Varshali, Sharma, Varsha, Varma, Sunita
• Format: Journal Article
• Language: English
• Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.02.2020
• Subjects: Algorithms; Chromaticity; Color imagery; Computer simulation; Computer vision; Image processing; Image segmentation; Luminosity; Markov analysis; Optimization
• ISSN: 2088-8708; 2088-8708
• DOI: 10.11591/ijece.v10i1.pp196-201

Region-based color image segmentation is elementary steps in image processing and computer vision. Color image segmentation is a region growing approach in which RGB color image is divided into the different cluster based on their pixel properties. The region-based color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, in which three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper, L*a*b color space conversion has been used to reduce the one dimension and geometrically it converts in the array hence the further one dimension has been reduced. This paper introduced an improved algorithm MMFO (Modified Moth Flame Optimization) Algorithm for RGB color image Segmentation which is based on bio-inspired techniques for color image segmentation. The simulation results of MMFO for region based color image segmentation are performed better as compared to PSO and GA, in terms of computation times for all the images. The experiment results of this method gives clear segments based on the different color and the different no. of clusters is used during the segmentation process.