Abstract
The objective of this work is to recognize object categories in paintings, such as cars, cows and cathedrals. We achieve this by training classifiers from natural images of the objects. We make the following contributions: (i) we measure the extent of the domain shift problem for image-level classifiers trained on natural images vs paintings, for a variety of CNN architectures; (ii) we demonstrate that classification-by-detection (i.e. learning classifiers for regions rather than the entire image) recognizes (and locates) a wide range of small objects in paintings that are not picked up by image-level classifiers, and combining these two methods improves performance; and (iii) we develop a system that learns a region-level classifier on-the-fly for an object category of a user’s choosing, which is then applied to over 60 million object regions across 210,000 paintings to retrieve localised instances of that category.
Original language | English |
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Title of host publication | Computer Vision -- ECCV 2016 Workshops |
Subtitle of host publication | Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part I |
Editors | Gang Hua, Hervé Jégou |
Place of Publication | Cham |
Publisher | Springer |
Pages | 721-737 |
Number of pages | 17 |
ISBN (Electronic) | 978-3-319-46604-0 |
DOIs | |
Publication status | E-pub ahead of print - 18 Sept 2016 |
Event | European Conference on Computer Vision 2016 Workshops - Amsterdam, Netherlands Duration: 8 Oct 2016 → 16 Oct 2016 http://www.eccv2016.org/workshops/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer International Publishing |
Volume | 9913 |
ISSN (Print) | 0302-9743 |
Conference
Conference | European Conference on Computer Vision 2016 Workshops |
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Abbreviated title | ECCV 2016 |
Country/Territory | Netherlands |
City | Amsterdam |
Period | 8/10/16 → 16/10/16 |
Internet address |