Segmentation of Infrared Images and Objectives Detection Using Maximum Entropy Method Based on the Bee Algorithm

Abstract

Thresholding is a popular image segmentation method that converts a gray-level image into a binary image. Many thresholding techniques have been proposed in the recent years. Among them, the maximum entropy thresholding has been widely applied. Image entropy thresholding approach has drawn the attentions in image segmentation. In this paper, the image thresholding approach with the index of entropy maximization of the grayscale histogram based on a novel optimization algorithm, namely, the bee algorithm is proposed to deal with infrared images. The bee algorithm is realized successfully in the process of solving the maximum entropy problem. The proposed algorithm uses the bee algorithm which proved to be the most powerful unbiased optimization technique for sampling a large solution space. Because of its unbiased stochastic sampling, it was quickly adapted in image processing and thus for infrared image segmentation as well. The experiments of segmenting of the infrared images are illustrated to prove that the proposed method can get ideal segmentation result with less computation cost. The proposed algorithm is also applied to the segmentation of standard images with very promising results.