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'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