Abstract
A recent trend in problem formulation in image segmentation is to employ the multi-objective optimization (MOO) methods. The decision-making MOOs are the collection of realistic complex optimization problems, where the objective functions are usually conflicting. Image segmentation is the clustering of pixels applying definite criteria. It is one of the crucial parts in image processing. This chapter provides a comprehensive survey on MOO encompassing image segmentation problems. Here, the segmentation models are categorized by the problem formulation with a relevant optimization scheme. The survey also provides the latest direction and challenges of MOO in image segmentation procedure.
Keywords
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Datta, N.S., Dutta, H.S., Majumder, K., Chatterjee, S., Wasim, N.A. (2018). A Survey on the Application of Multi-Objective Optimization Methods in Image Segmentation. In: Mandal, J., Mukhopadhyay, S., Dutta, P. (eds) Multi-Objective Optimization. Springer, Singapore. https://doi.org/10.1007/978-981-13-1471-1_12
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