A new technique for multilevel color image thresholding based on modified fuzzy entropy and Lévy flight firefly algorithm. (August 2018)

• Record Type: Journal Article
• Authors: Pare, S.; Bhandari, A.K.; Kumar, A.; Singh, G.K.
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 476
• Page End: 495
• Publication Date: 2018-08
• Subjects: Color image segmentation -- Multi-level thresholding -- Modified fuzzy entropy -- Lévy flight guided firefly algorithm; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.08.008
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: In this paper, a modified fuzzy entropy function is proposed using Lévy flight guided firefly algorithm. Modified fuzzy entropy function is the difference of adjacent entropies. In this approach, most widely used meta-heuristic techniques algorithms are exploited. The experimental results reveal better performance of the proposed approach over other methods. The validity of the proposed technique is reported both qualitatively and quantitatively. Abstract: In this paper, a modified fuzzy entropy (MFE) function is proposed to perform the multilevel thresholding of color images at different segmentation levels using Lévy flight guided firefly algorithm (LFA). Modified fuzzy entropy function is the difference of adjacent entropies. Therefore, minimizing the fitness function will provide thresholding levels such that all the regions have almost equal entropies. LFA algorithm improves the search performance and gains optimal threshold values for an efficient segmentation of colored images and satellite images. A comparative study of different nature inspired algorithms using MFE as an objective function presented. The study proves that the proposed MFE-LFA algorithm exhibits better performance in terms of different fidelity parameters and computation time. In addition, the proposed method is also compared with the most widely used Kapur's entropy based segmentation approaches, where the simulation results show the proposed methodology as the most efficient and effective algorithm. Graphical abstract: