Abstract
This study proposes an improved cooperative quantum-behaved particle swarm optimisation (ICQPSO) algorithm to find multiple threshold levels for colour images with multilevel Renyi entropy (MRE). In the proposed method, the context vector of each particle is updated each time dynamically when a cooperation operation is completed with other particles. The improved search ability and optimisation performance of ICQPSO algorithm with MRE (hence called MRE-ICQPSO) extensively investigated with other well known nature-inspired algorithms such as Levi flight-guided firefly, cuckoo search, artificial bee colony, and beta differential evolution. The proposed method is applied to the Berkley segmentation dataset with 300 distinct colour images to show the effective performance of the algorithm in terms of fidelity parameters and computation time.
Published Version
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.
