- Tytuł:
- 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 - Powiązania:
- https://bibliotekanauki.pl/articles/91856.pdf
- Data publikacji:
- 2020
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
image retrieval
fuzzy rules
local image features
pobieranie obrazu
lokalne funkcje obrazu - Opis:
- 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.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 1; 57-69
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł