Abstract
In this paper, we presented a new spatial fuzzy clustering algorithm optimised by the Artificial Bees Colony (ABC) algorithm. By introducing the global search of ABC algorithm into the spatial Fuzzy C-Means Clustering algorithm, a new kind of methods (SFCM-ABC) is proposed. SFCM-ABC has two major characteristics. First it tackles better noisy image segmentation by making use of the spatial local information into the membership function. Secondly, it improves the global performance by taking advantages of the globalized search in the entire solution space of ABC rather than a localized searching like in FCM algorithm. Experiments with synthetic and real images show that SFCM-ABC is robust to noise compared to other methods. DOI: http://dx.doi.org/10.11591/ij-ai.v1i3.765 Full Text: PDF
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IAES International Journal of Artificial Intelligence (IJ-AI)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
