Temat: content-based image retrieval - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Fast image index for database management engines
Autorzy:: Grycuk, Rafał
Najgebauer, Patryk
Kordos, Miroslaw
Scherer, Magdalena M.
Marchlewska, Alina
Powiązania:: https://bibliotekanauki.pl/articles/1837480.pdf
Data publikacji:: 2020
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: image descriptor
content-based image retrieval
image indexing
Opis:: 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.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 2; 113-123
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Semantic hashing for fast solar magnetogram retrieval
Autorzy:: Grycuk, Rafał
Scherer, Rafał
Marchlewska, Alina
Napoli, Christian
Powiązania:: https://bibliotekanauki.pl/articles/2147145.pdf
Data publikacji:: 2022
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: content-based image retrieval
image descriptor
solar analysis
Opis:: 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.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 4; 299--306
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

Artykuł

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