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Automatic clustering of colour images using quantum inspired meta-heuristic algorithms

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10252037" target="_blank" >RIV/61989100:27240/22:10252037 - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/article/10.1007/s10489-022-03806-8#citeas" target="_blank" >https://link.springer.com/article/10.1007/s10489-022-03806-8#citeas</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s10489-022-03806-8" target="_blank" >10.1007/s10489-022-03806-8</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Automatic clustering of colour images using quantum inspired meta-heuristic algorithms

  • Original language description

    This work explores the effectiveness and robustness of quantum computing by conjoining the principles of quantum computing with the conventional computational paradigm for the automatic clustering of colour images. In order to develop such a computationally efficient algorithm, two population-based meta-heuristic algorithms, viz., Particle Swarm Optimization (PSO) algorithm and Enhanced Particle Swarm Optimization (EPSO) algorithm have been consolidated with the quantum computing framework to yield the Quantum Inspired Particle Swarm Optimization (QIPSO) algorithm and the Quantum Inspired Enhanced Particle Swarm Optimization (QIEPSO) algorithm, respectively. This paper also presents a comparison between the proposed quantum inspired algorithms with their corresponding classical counterparts and also with three other evolutionary algorithms, viz., Artificial Bee Colony (ABC), Differential Evolution (DE) and Covariance Matrix Adaption Evolution Strategies (CMA-ES). In this paper, twenty different sized colour images have been used for conducting the experiments. Among these twenty images, ten are Berkeley images and ten are real life colour images. Three cluster validity indices, viz., PBM, CS-Measure (CSM) and Dunn index (DI) have been used as objective functions for measuring the effectiveness of clustering. In addition, in order to improve the performance of the proposed algorithms, some participating parameters have been adjusted using the Sobol&apos;s sensitivity analysis test. Four segmentation evaluation metrics have been used for quantitative evaluation of the proposed algorithms. The effectiveness and efficiency of the proposed quantum inspired algorithms have been established over their conventional counterparts and the three other competitive algorithms with regards to optimal computational time, convergence rate and robustness.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Applied Intelligence

  • ISSN

    0924-669X

  • e-ISSN

    1573-7497

  • Volume of the periodical

    2022

  • Issue of the periodical within the volume

    May

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    23

  • Pages from-to

    nestrankovano

  • UT code for WoS article

    000840062800001

  • EID of the result in the Scopus database

    2-s2.0-85135855632