Analysis of Histogram and Energy Curve-based Thresholding using Cuckoo Search

In recent years, optimization has become the most notable area of research. Optimization is applied to spot the most favourable and ideal solution that provides the best results under certain constraints. Nature-inspired algorithms can be used to find out the most favourable solution among the computed solutions. Several nature-inspired algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Honey Bee Optimization (HBO), Lion Optimization Algorithm (LOA), etc. can be employed to procure the most favourable solutions in less time. Cuckoo search has emerged as the uncomplicated and the most prolific and productive algorithm to fix the real optimization issues that are extremely non-linear. Image Segmentation is said to be the most demanding task for recollecting non-converging analogous regions. The major challenge is to calculate the non-converging analogous regions. An immense requisite is there to initiate an intelligent image segmentation technique that fulfils both the above challenges and offers the most favorable results for image segmentation. In this paper, the author has appertained the population-based cuckoo search algorithm. This paper compares Energy Curve-based Thresholding using Cuckoo Search and Histogram -based Thresholding using Cuckoo Search.