Chapter 17 - CSBIIST: cuckoo search-based intelligent image segmentation technique

Abstract

Optimization has become the most prominent area of research in recent years. Nature-inspired algorithms can be employed to extract the most optimal solution among computed solutions. There exist plenty of nature-inspired algorithms, such as the genetic algorithm, particle swarm optimization, bumble bee mating, artificial bee colony, elephant herding, the firefly algorithm, and the cuckoo search algorithm, which can be employed to procure optimal solutions in less time. Image segmentation employs an unsupervised learning model and is recognized as a most demanding task to recollect nonconverging analogous regions. The most important challenge is to compute count of the same. There is an immense requisite to commence an intelligent technique which fulfills both aforementioned challenges. We have employed the cuckoo search algorithm as it has emerged as the simplest and most prolific algorithm to resolve real optimization issues that are extremely nonlinear. We have compared the technique with state-of-the-art techniques to evince its efficacy.