Efficient image retrieval by fuzzy rules from boosting and metaheuristic
| Autor: | Marcin Korytkowski, Rafal A. Angryk, Agnieszka Siwocha, Roman Senkerik, Magdalena Scherer, Mirosław Kordos |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Boosting (machine learning) Higher education Computer science business.industry fuzzy rules media_common.quotation_subject 02 engineering and technology local image features Fuzzy logic image retrieval 020901 industrial engineering & automation Artificial Intelligence Hardware and Architecture Excellence Modeling and Simulation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Metaheuristic Image retrieval Information Systems media_common |
| Zdroj: | Journal of Artificial Intelligence and Soft Computing Research |
| Popis: | 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. © 2020 Marcin Korytkowski et al., published by Sciendo. program of the Polish Minister of Science and Higher Education under the name "Regional Initiative of Excellence" in the years 2019-2022 [020/RID/2018/19] |
| Databáze: | OpenAIRE |
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