Title: DeepPainter: Painter Classification Using Deep Convolutional Autoencoders,
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.