Abstract
In this paper, we present a multi-objective segmentation approach for color images. Three objectives, overall deviation, edge value, and connectivity measure, are optimized simultaneously using a multi-objective evolutionary algorithm (MOEA). To demonstrate the effectiveness of the proposed approach, experiments are conducted on benchmark images. The results justify that the proposed approach is able to partition color images in a number of segments consistent with human visual perception. For quantitative evaluation, we extend the existing Probabilistic Rand Index (PRI) considering multi-objective segmentation. The outcomes show that the proposed approach can obtain non-dominated and near-optimal segment solutions satisfying several criteria simultaneously. It can also find the correct number of segments automatically.
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 callDisclaimer: 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.
