Artificial Bee Colony: Theory, Literature Review, and Application in Image Segmentation

Abstract

The artificial bee colony (ABC) is one of the most well-regarded swarm intelligence-based algorithms in the literature of evolutionary computation techniques. This algorithm mimics the foraging behaviors of bees in the hive. Due to its well-designed search ability and dependency on fewer control parameters, ABC has extensive usage in various fields. One of the remarkable areas where ABC has been successfully implemented is the image segmentation. Image segmentation is the process of dividing a digital image into multiple segments. The overall goal of image segmentation is to extract meaningful information from an image. Although considerable attempts have been made to develop image segmentation techniques using ABC, it is not possible to find any work in the literature that particularly seeks to reflect the profile of ABC-based image segmentation techniques. This chapter first tries to describe ABC-based image segmentation techniques from the fundamental concepts of segmentation such as clustering, thresholding, and edge detection. The chapter also applies ABC algorithm to a challenging task in image segmentation. It is observed that ABC can accurately segment the regions of an image.