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Automatic fuzzy genetic algorithm in clustering for images based on the extracted intervals

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Abstract

This research proposes the method to extract the characteristics of images to become the intervals. These intervals are used to build the automatic fuzzy genetic algorithm for images (AFGI). In the proposed model, the overlap measure is the criterion to evaluate the closeness of intervals, and the new Davies and Bouldin index is the objective function. The AFGI can determine the proper number of clusters, the images in each cluster, and the probability to belong to clusters of images at the same time. The experiments with different types of images illustrate the steps of AFGI, and show its significant benefit in comparing to other algorithms.

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Authors and Affiliations

  1. University of Science, Ho Chi Minh City, Vietnam

    Dinh Phamtoan

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Dinh Phamtoan

  3. Faculty of Engineering, Van lang University, Ho Chi Minh City, Vietnam

    Dinh Phamtoan

  4. College of Natural Science, Can Tho University, Can Tho, Vietnam

    Tai Vovan

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  1. Dinh Phamtoan
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  2. Tai Vovan
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Correspondence to Tai Vovan.

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Appendix A

Appendix A

Table 8 The created chromosomes by operators in first iteration
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Phamtoan, D., Vovan, T. Automatic fuzzy genetic algorithm in clustering for images based on the extracted intervals. Multimed Tools Appl 80, 35193–35215 (2021). https://doi.org/10.1007/s11042-020-09975-3

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  • DOI: https://doi.org/10.1007/s11042-020-09975-3

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