Celebratio Mathematica — Daubechies

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R. Yin, E. Monson, E. Honig, I. Daubechies, and M. Maggioni: “Object recognition in art drawings: Transfer of a neural network,” pp. 2299–2303 in 2016 IEEE international conference on acoustics, speech and signal processing (Shanghai, 20–25 March 2016). IEEE (Piscataway, NJ), 2016. incollection

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 classification 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.

Rujie (Rachel) Yin	Related
Mauro Maggioni	Related
Elizabeth Honig	Related
Eric E. Monson	Related