DeepPainter: Painter Classification Using Deep Convolutional Autoencoders

Conference paper

DOI: 10.1007/978-3-319-44781-0_3

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9887)
Cite this paper as:
David O.E., Netanyahu N.S. (2016) DeepPainter: Painter Classification Using Deep Convolutional Autoencoders. In: Villa A., Masulli P., Pons Rivero A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science, vol 9887. Springer, Cham

Abstract

In this paper we describe the problem of painter classification, and propose a novel approach based on deep convolutional autoencoder neural networks. While previous approaches relied on image processing and manual feature extraction from paintings, our approach operates on the raw pixel level, without any preprocessing or manual feature extraction. We first train a deep convolutional autoencoder on a dataset of paintings, and subsequently use it to initialize a supervised convolutional neural network for the classification phase.

The proposed approach substantially outperforms previous methods, improving the previous state-of-the-art for the 3-painter classification problem from 90.44 % accuracy (previous state-of-the-art) to 96.52 % accuracy, i.e., a 63 % reduction in error rate.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceBar-Ilan UniversityRamat-GanIsrael
  2. 2.Center for Automation ResearchUniversity of MarylandCollege ParkUSA

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