Image Segmentation as an Important Step in Image-Based Digital Technologies in Smart Cities: A New Nature-Based Approach

Abstract

Digital technologies are one of the main components in smart cities. Images are one of the principal data types in digital technologies. Images can be seen in a variety of applications such as intelligent transport systems, tourism applications, and real-time science understanding. Therefore, it is important to provide efficient image processing algorithms in this context. One of the primary operations in image analysis is image segmentation. Image segmentation is the process of partitioning an image into multiple regions with same semantic contents. Image thresholding considers as a popular method for image segmentation. So far, many approaches have been proposed for image thresholding. Maximum entropy thresholding has been widely applied in the literature. This paper proposes a multilevel image thresholding (MECOAT) using cuckoo optimization algorithm (COA). COA is a new nature-based optimization algorithm which is inspired by a bird named cuckoo. This algorithm is based unusual egg laying and breeding of cuckoos. MECOAT tries to maximize entropy criterion. Three different algorithms are compared with MECOAT algorithm: particle swarm optimization, genetic algorithm, and bat algorithm. Experimental results indicate that MECOAT presents better results in terms of fitness value, peak signal to noise ratio (PSNR) and robustness in most cases.