Multiobjective clustering with metaheuristic: current trends and methods in image segmentation

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Multiobjective clustering with metaheuristic: current trends and methods in image segmentation

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This study reviews the state-of-the-art multiobjective optimisation (MOO) techniques with metaheuristic through clustering approaches developed specifically for image segmentation problems. The authors treat image segmentation as a real-life problem with multiple objectives; thus, focusing on MOO methods that allow a trade-off among multiple objectives. A reasonable solution to a multiobjective (MO) problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. The primary difference of MOO methods from traditional image segmentation is that instead of a single solution, their output is a set of solutions called Pareto-optimal solution. This study discusses the evolutionary and non-evolutionary MO clustering techniques for image segmentation. It diagnoses the requirements and issues for modelling MOO via MO clustering technique. In addition, the potential challenges and the directions for future research are presented.

Inspec keywords: pattern clustering; image segmentation; Pareto optimisation; evolutionary computation

Other keywords: MOO method; Pareto-optimal solution; state-of-the-art multiobjective optimisation technique; nonevolutionary MO clustering technique; multiobjective clustering; real-life problem; image segmentation; multiobjective problem

Subjects: Computer vision and image processing techniques; Optimisation techniques; Image recognition; Optical, image and video signal processing; Optimisation techniques

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