Fish school search approach to find optimized thresholds in gray-scale image

Image thresholding is one essential feature of modern digital image processing, which is meant for the segmentation of image into several parts. It helps to understand the image and to detect particular objects from the image. There are various soft computing based image thresholding techniques evolved till date using advance tools such as fuzzy operator, genetic algorithm, swarm optimization etc. In this paper, Fish school search (FSS), a new swarm optimization technique is applied to find the optimized threshold value to segment the image. FSS mimics the fishes searching for food in group, considering an aquarium as search space. The proposed technique described in this paper shows optimized result comparing to traditional image thresholding technique.