Abstract
Image segmentations requirement is everywhere from autonomous car driving, satellite images, to medical diagnosis. Various computer vision applications rely on image segmentation. Frequently, digital images are divided into many segments (pixel sets, also called image objects) through partitioning. Image segmentation involves transforming the representation of an image into a more meaningful and easy to analyze format. In order to achieve image segmentation, there are several possibilities, and there is usually a trade-off between them, for example, minimizing complexity and maximizing diversity measures during the image segmentation process. The use of multi-objective optimization is an emerging trend in problem formulation for image segmentation to resolve this trade-off. A number of literatures have explored multi-objective optimization along with metaheuristic algorithms. This chapter discusses the role of multi-objective optimization in image segmentation and classification. Furthermore, it offers a comprehensive review of multi-objective metaheuristic evolutionary algorithms applied to remote sensing and medical domains. The purpose of this research is to highlight the significance of multi-objective metaheuristic evolutionary algorithms, current trends, and future scope of image segmentation.
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