Efficient image retrieval by fuzzy rules from boosting and metaheuristic

• Autorzy: Korytkowski Marcin , Senkerik Roman , Scherer Magdalena M. , Angryk Rafal A. , Kordos Miroslaw , Siwocha Agnieszka

Identyfikatory

DOI

10.2478/jaiscr-2020-0005

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

image retrieval; fuzzy rules; local image features

PL

pobieranie obrazu; lokalne funkcje obrazu

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2020
• Tom: Vol. 10, No. 1
• Strony: 57--69
• Opis fizyczny: Bibliogr. 85 poz., rys.

Twórcy

autor

Korytkowski Marcin

• Department of Computer Engineering, Czętochowa University of Technology, Częstochowa, Poland

autor

Senkerik Roman

• Tomas Bata University in Zlín, 760 05 Zlín, Czech Republic

autor

Scherer Magdalena M.

• Faculty of Management, Częstochowa University of Technology, Częstochowa, Poland

autor

Angryk Rafal A.

• Department of Computer Science, Georgia State University, Atlanta, GA, USA

autor

Kordos Miroslaw

• Department of Computer Science and Automatics, University of Bielsko-Biała, Poland

autor

Siwocha Agnieszka

• Information Technology Institute, University of Social Science, Łodź, Poland
• Clark University, Worcester, MA 01610, USA

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).