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Image Segmentation Using Artificial Bee Colony and Fast Fuzzy C-Means Algorithms

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Fuzzy c-means (FCM) has found a variety of applications in image segmentation. Unfortunately, traditional FCM algorithms tend to be relatively high cost. In this letter, we present a novel image segmentation approach based on Fast Fuzzy c-means (FFCM) and artificial bee colony algorithm (ABC). The proposed approach yields superior subjectivity and objective evaluation with traditional fast FCM algorithm (T-FFCM), fast FCM based on genetic algorithm (GA-FFCM), and fast FCM based on particle swarm optimization (PSO-FFCM) as verified by experimental results.
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Keywords: ARTIFICIAL BEE COLONY ALGORITHM; FAST FCM; IMAGE SEGMENTATION

Document Type: Research Article

Publication date: 2012-03-01

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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