Efficient image retrieval by fuzzy rules from boosting and metaheuristic

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F20%3A63525241" target="_blank" >RIV/70883521:28140/20:63525241 - isvavai.cz</a>
Result on the web
<a href="https://content.sciendo.com/configurable/contentpage/journals$002fjaiscr$002f10$002f1$002farticle-p57.xml" target="_blank" >https://content.sciendo.com/configurable/contentpage/journals$002fjaiscr$002f10$002f1$002farticle-p57.xml</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.2478/jaiscr-2020-0005" target="_blank" >10.2478/jaiscr-2020-0005</a>

Result language
angličtina
Original language name
Efficient image retrieval by fuzzy rules from boosting and metaheuristic
Original language description
Fast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter.
Czech name
—
Czech description
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Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical
Journal of Artificial Intelligence and Soft Computing Research
ISSN
2083-2567
e-ISSN
—
Volume of the periodical
10
Issue of the periodical within the volume
1
Country of publishing house
PL - POLAND
Number of pages
13
Pages from-to
57-69
UT code for WoS article
000502574500005
EID of the result in the Scopus database
2-s2.0-85077117526

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