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2 Journal of Artificial Intelligence and Soft Computing Research

2 2020

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Fast image index for database management engines

Grycuk Rafał, Najgebauer Patryk, Kordos Miroslaw, Scherer Magdalena M., Marchlewska Alina

Journal of Artificial Intelligence and Soft Computing Research

2020

Vol. 10, No. 2

113--123

Large-scale image repositories are challenging to perform queries based on the content of the images. The paper proposes a novel, nested-dictionary data structure for indexing image local features. The method transforms image local feature vectors into two-level hashes and builds an index of the content of the images in the database. The algorithm can be used in database management systems. We implemented it with an example image descriptor and deployed in a relational database. We performed the experiments on two image large benchmark datasets.

Efficient image retrieval by fuzzy rules from boosting and metaheuristic

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

Journal of Artificial Intelligence and Soft Computing Research

2020

Vol. 10, No. 1

57--69

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.