Abstract / Description of output
The objective of this work is to find objects in paintings by learning object-category classifiers from available sources of natural images. Finding such objects is of much benefit to the art history community as well as being a challenging problem in large-scale retrieval and domain adaptation.
We make the following contributions: (i) we show that object classifiers, learnt using Convolutional Neural Networks (CNNs) features computed from various natural image sources, can retrieve paintings containing these objects with great success; (ii) we develop a system that can learn object classifiers on-the-fly from Google images and use these to find a large variety of previously unfound objects in a dataset of 210,000 paintings; (iii) we combine object classifiers and detectors to align objects to allow for direct comparison; for example to illustrate how they have varied over time.
We make the following contributions: (i) we show that object classifiers, learnt using Convolutional Neural Networks (CNNs) features computed from various natural image sources, can retrieve paintings containing these objects with great success; (ii) we develop a system that can learn object classifiers on-the-fly from Google images and use these to find a large variety of previously unfound objects in a dataset of 210,000 paintings; (iii) we combine object classifiers and detectors to align objects to allow for direct comparison; for example to illustrate how they have varied over time.
Original language | English |
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Title of host publication | Computer Vision - ECCV 2014 Workshops |
Subtitle of host publication | Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part I |
Editors | Lourdes Agapito, Michael M. Bronstein, Carsten Rother |
Place of Publication | Cham |
Publisher | Springer |
Pages | 54-70 |
Number of pages | 17 |
ISBN (Electronic) | 978-3-319-16178-5 |
ISBN (Print) | 978-3-319-16177-8 |
DOIs | |
Publication status | Published - 2015 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer International Publishing |
Volume | 8925 |
ISSN (Print) | 0302-9743 |
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Elliot Crowley
- School of Engineering - Senior Lecturer
- Data Science and Artificial Intelligence
Person: Academic: Research Active