2D art recognition in uncontrolled conditions using one-shot learning
| Autor: | Dmitry P. Nikolaev, Natalya Skoryukina, Vladimir V. Arlazarov |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Contextual image classification
Computer science business.industry Pattern recognition Image processing 02 engineering and technology One-shot learning 03 medical and health sciences Reference image 0302 clinical medicine Robustness (computer science) 030221 ophthalmology & optometry 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Geometric modeling business Mobile device Constellation |
| Zdroj: | ICMV |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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