A dynamic fuzzy genetic algorithm for natural image segmentation using adaptive mean shift. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Arfan Jaffar, M.
• Is Part Of: Journal of experimental & theoretical artificial intelligence. Volume 29:Issue 1(2017)
• Page Start: 149
• Page End: 156
• Publication Date: 2017-01-02
• Subjects: Adaptive mean shift (AMS) -- segmentation -- spatial fuzzy c-mean (sFCM) -- genetic algorithm (GA); Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/0952813X.2015.1132263
• Languages: English
• ISSNs: 0952-813X

Abstract

Abstract: In this paper, a colour image segmentation approach based on hybridisation of adaptive mean shift (AMS), fuzzy c-mean and genetic algorithms (GAs) is presented. Image segmentation is the perceptual faction of pixels based on some likeness measure. GA with fuzzy behaviour is adapted to maximise the fuzzy separation and minimise the global compactness among the clusters or segments in spatial fuzzy c-mean (sFCM). It adds diversity to the search process to find the global optima. A simple fusion method has been used to combine the clusters to overcome the problem of over segmentation. The results show that our technique outperforms state-of-the-art methods.