Abstract
AbstractPolygonal approximation (PA) is a challenging problem in representation of images in computer vision, pattern recognition and image analysis. This paper proposes a stochastic technique-based firefly algorithm (FA) for PA. This technique customizes a kind of randomization by searching a set of solutions. In contrast, PA requires more combination of approximation to find an optimal solution. The algorithm involves several steps to produce better results. The attractiveness and brightness of the firefly have been used efficiently to solve the approximation problem. While compared to other similar algorithms, FA is independent of velocities which are considered as an advantage for this algorithm. Subsequently, the multi-swarm nature of FA allows finding multiple optimal solutions concurrently. This technique achieves the main goal of PA that is minimum error value with less number of dominant points. The experimental results show that proposed algorithm generates better solutions compared to other algorithms.KeywordsPolygonal approximationFirefly algorithmError valueDominant pointMulti-swarm natureVelocitiesOptimal solutions
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.
