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A population-based automatic clustering algorithm for image segmentation

Published: 08 July 2021 Publication History

Abstract

Clustering is one of the prominent approaches for image segmentation. Conventional algorithms such as k-means, while extensively used for image segmentation, suffer from problems such as sensitivity to initialisation and getting stuck in local optima. To overcome these, population-based metaheuristic algorithms can be employed. This paper proposes a novel clustering algorithm for image segmentation based on the human mental search (HMS) algorithm, a powerful population-based algorithm to tackle optimisation problems. One of the advantages of our proposed algorithm is that it does not require any information about the number of clusters. To verify the effectiveness of our proposed algorithm, we present a set of experiments based on objective function evaluation and image segmentation criteria to show that our proposed algorithm outperforms existing approaches.

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  • (2022)An improved multi-population whale optimization algorithmInternational Journal of Machine Learning and Cybernetics10.1007/s13042-022-01537-313:9(2447-2478)Online publication date: 11-Apr-2022

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    cover image ACM Conferences
    GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion
    July 2021
    2047 pages
    ISBN:9781450383516
    DOI:10.1145/3449726
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    Published: 08 July 2021

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    Author Tags

    1. automatic clustering
    2. human mental search
    3. image segmentation
    4. optimisation
    5. population-based algorithms

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    • (2022)An improved multi-population whale optimization algorithmInternational Journal of Machine Learning and Cybernetics10.1007/s13042-022-01537-313:9(2447-2478)Online publication date: 11-Apr-2022

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