<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Department of Computer Science and Automatics

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic"}

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...

Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Publicado en línea: 11 dic 2019

Páginas: 57 - 69

Recibido: 14 sept 2019

Aceptado: 16 nov 2019

DOI: https://doi.org/10.2478/jaiscr-2020-0005

Palabras clave
image retrieval, fuzzy rules, local image features

© 2020 Marcin Korytkowski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Publicado en línea: 11 dic 2019

Páginas: 57 - 69

Recibido: 14 sept 2019

Aceptado: 16 nov 2019

DOI: https://doi.org/10.2478/jaiscr-2020-0005

Palabras claveimage retrieval, fuzzy rules, local image features

© 2020 Marcin Korytkowski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Palabras clave
image retrieval, fuzzy rules, local image features