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2D art recognition in uncontrolled conditions using one-shot learning
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Paper Abstract

The paper considers the problem of 2D art identification in photos acquired with mobile devices under the conditions of museum exhibition. The proposed approach is based on a compact description of an image with a constellation of keypoints and corresponding local descriptors. A two-step comparison scheme is described for finding the best reference image matching the query. Bag-of-features approach is used as a first step, then mutual disposition of points is analyzed. Rejection of the query is performed if no suitable matches are found. Geometrical normalization of the query image is proposed to achieve higher robustness against scale and viewpoint variations. After the normalization, mutual disposition of points is estimated using a simplified geometric model. Advantages of the described approach over state-of-the-art solutions are considered. The results of the experiments conducted on the open WikiArt dataset are presented along with processing times for different hardware platforms.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412E (15 March 2019); doi: 10.1117/12.2523017
Show Author Affiliations
Natalya S. Skoryukina, Smart Engines Ltd. (Russian Federation)
Dmitry P. Nikolaev, Smart Engines Ltd. (Russian Federation)
Federal Research Ctr. (Russian Federation)
Moscow Institute of Physics and Technology (Russian Federation)
Vladimir V. Arlazarov, Smart Engines Ltd. (Russian Federation)
Federal Research Ctr. (Russian Federation)
Moscow Institute of Physics and Technology (Russian Federation)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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