Object recognition in art drawings: Transfer of a neural network

Conference Paper

© 2016 IEEE. We consider the problem of recognizing objects in collections of art works, in view of automatically labeling, searching and organizing databases of art works. To avoid manually labelling objects, we introduce a framework for transferring a convolutional neural network (CNN), trained on available large collections of labelled natural images, to the context of drawings. We retrain both the top and the bottom layer of the network, responsible for the high-level classiication output and the low-level features detection respectively, by transforming natural images into drawings. We apply this procedure to the drawings in the Jan Brueghel Wiki, and show the transferred CNN learns a discriminative metric on drawings and achieves good recognition accuracy. We also discuss why standard descriptor-based methods is problematic in the context of drawings.

Full Text

Duke Authors

Cited Authors

  • Yin, R; Monson, E; Honig, E; Daubechies, I; Maggioni, M

Published Date

  • May 18, 2016

Published In

Volume / Issue

  • 2016-May /

Start / End Page

  • 2299 - 2303

International Standard Serial Number (ISSN)

  • 1520-6149

International Standard Book Number 13 (ISBN-13)

  • 9781479999880

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2016.7472087

Citation Source

  • Scopus