Image Segmentation Using Artificial Bee Colony and Fast Fuzzy C-Means Algorithms
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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
Document Type: Research Article
Publication date: 2012-03-01
More about this publication?
- 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.
- Editorial Board
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites