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Published in:
2015 | OriginalPaper | Chapter
In Search of Art
Authors : Elliot J. Crowley, Andrew Zisserman
Published in: Computer Vision - ECCV 2014 Workshops
Publisher: Springer International Publishing
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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.