Abstract
A kind of image segmentation method with two-dimensional cross entropy was proposed based on the artificial bee colony algorithm to overcome the large amount of calculation and long computing time. Firstly, the principle of two-dimensional cross entropy threshold segmentation was analyzed. Then, the bionic mechanism and searching optimization process of the artificial bee colony algorithm were analyzed, and the threshold segmentation method of two-dimensional cross entropy combined with artificial bee colony algorithm was proposed. Finally, typical image segmentation experiments by using the proposed method were performed and the results were compared with two-dimensional cross entropy exhaustive segmentation method and two-dimensional entropy segmentation method based on Particle Swarm Optimization (PSO). Experimental results show that the speed of the proposed method is ten times faster than the two-dimensional entropy exhaustive segmentation method respectively. Moreover, the threshold selection accuracy and running speed of the proposed method are both better than the threshold segmentation method of two-dimensional cross entropy based on PSO. Therefore, the image segmentation method of two-dimensional cross entropy based on artificial bee colony algorithm can quickly and efficiently resolve image segmentation problems.
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.
