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3 Journal of Artificial Intelligence and Soft Computing Research

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Semantic hashing for fast solar magnetogram retrieval

Grycuk Rafał, Scherer Rafał, Marchlewska Alina, Napoli Christian

Journal of Artificial Intelligence and Soft Computing Research

2022

Vol. 12, No. 4

299--306

We propose a method for content-based retrieving solar magnetograms. We use the SDO Helioseismic and Magnetic Imager output collected with SunPy PyTorch libraries. We create a mathematical representation of the magnetic field regions of the Sun in the form of a vector. Thanks to this solution we can compare short vectors instead of comparing full-disk images. In order to decrease the retrieval time, we used a fully-connected autoencoder, which reduced the 256-element descriptor to a 32-element semantic hash. The performed experiments and comparisons proved the efficiency of the proposed approach. Our approach has the highest precision value in comparison with other state-of-the-art methods. The presented method can be used not only for solar image retrieval but also for classification tasks.

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.

Detecting visual objects by edge crawling

Grycuk Rafał, Wojciechowski Adam, Wei Wei, Siwocha Agnieszka

Journal of Artificial Intelligence and Soft Computing Research

2020

Vol. 10, No. 3

223--237

Content-based image retrieval methods develop rapidly with a growing scale of image repositories. They are usually based on comparing and indexing some image features. We developed a new algorithm for finding objects in images by traversing their edges. Moreover, we describe the objects by histograms of local features and angles. We use such a description to retrieve similar images fast. We performed extensive experiments on three established image datasets proving the effectiveness of the proposed method.