Quantum Spider Monkey Optimization (QSMO) Algorithm for Automatic Gray-Scale Image Clustering
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10241744" target="_blank" >RIV/61989100:27240/19:10241744 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8554872" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8554872</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICACCI.2018.8554872" target="_blank" >10.1109/ICACCI.2018.8554872</a>
Alternative languages
Result language
angličtina
Original language name
Quantum Spider Monkey Optimization (QSMO) Algorithm for Automatic Gray-Scale Image Clustering
Original language description
In automatic image clustering, high homogeneity of each cluster is always desired. The increase in number of thresholds in gray scale image segmentation/clustering poses various challenges. Recent times have witnessed the growing popularity of swarm intelligence based algorithms in the field of image segmentation. The Spider Monkey Optimization (SMO) algorithm is a notable example, which is motivated by the intelligent behavior of the spider monkeys. The SMO is broadly categorized as a fission-fusion social structure based intelligent algorithm. The original version of the algorithm as well as its variants have been successfully used in several optimization problems. The current work proposes a quantum version of SMO algorithm which takes recourse to quantum encoding of its population along with quantum variants of the intrinsic operations. The basic concepts and principles of quantum mechanics allows QMSO to explore the power of computing. In QMSO, qubits designated chromosomes operate to drive the solution toward better convergence incorporating rotation gate in Hilbert hyperspace. A fitness function associated with maximum distance between cluster centers have been introduced. An application of the proposed QSMO algorithm is demonstrated on the determination of automatic clusters from real life images. A comparative study with the performance of the classical SMO shows the efficacy of the proposed QSMO algorithm.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018
ISBN
978-1-5386-5314-2
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
1869-1874
Publisher name
IEEE
Place of publication
Piscataway
Event location
Bengalúr
Event date
Sep 19, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000455682100317