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Erschienen in:
Face Recognition from Multiple Stylistic Sketches: Scenarios, Datasets, and Evaluation Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

The Art of Detection

verfasst von : Elliot J. Crowley, Andrew Zisserman

Erschienen in: Computer Vision – ECCV 2016 Workshops

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel LOH and Behold: Web-Scale Visual Search, Recommendation and Clustering Using Locally Optimized Hashing
Nächstes Kapitel A Streamlined Photometric Stereo Framework for Cultural Heritage
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Metadaten
Titel
The Art of Detection
verfasst von
Elliot J. Crowley
Andrew Zisserman
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016 Workshops

Print ISBN: 978-3-319-46603-3

Electronic ISBN: 978-3-319-46604-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46604-0_50

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