Ref: International Conference on Artificial Neural Networks (ICANN), Springer LNCS,
Vol. 9887, pp. 20–28, Barcelona, Spain, September 2016.

DeepPainter: Painter Classification Using Deep
Convolutional Autoencoders
Eli (Omid) David1 and Nathan S. Netanyahu1,2
1

arXiv:1711.08763v1 [] 23 Nov 2017

2

Department of Computer Science, Bar-Ilan University, Ramat-Gan, Israel
mail@elidavid.com, nathan@cs.biu.ac.il
Center for Automation Research, University of Maryland, College Park, MD, USA
nathan@cfar.umd.edu

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.

1

Introduction

Art forgery, which dates back more than two thousand years, has played a key role in
the development of painting authentication. This task has been usually performed
manually by art experts who have dedicated their lives to this profession. Their
expertise amounted to using various characteristics other than what the human eye
can see, including chemical analysis, spectrometry, and infrared or X-ray imaging.
The infamous Vermeer forgery [12] attests, perhaps, most vividly to the challenges
presented by painting authentication. Han van Meegeren used historical canvasses
and managed to deceive art experts into believing that his painting was an authentic Vermeer. Only after being charged with treason and sentenced to death for
selling another (forged) Vermeer, did he confess and was forced to create another
painting to prove himself innocent of treason. A more recent case of painting authenticity involves the Pollock paintings found a decade ago in a storage locker in
Wainscott, NY. The authenticity of these paintings was compromised on the basis
of computer analysis of the paintings’ fractal dimension [14]. This claim was subsequently disputed by analyzing childlike drawings that supposedly have the same
fractal dimension as the Pollock paintings [3].
In this paper we address the closely related problem of painting classification, i.e.,
the task of assigning a specific artist to a given painting (from a dataset of paintings
by several artists). Note that the image authentication problem can be viewed as a
binary image classification problem (i.e., determine whether or not a given painting
was painted by a certain artist). Recent developments for both problem types have
focused on preprocessing techniques of reducing the high dimensionality of visual

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E.O. David, N.S. Netanyahu

data to low-dimensional representations which can be manipulated towards image
understanding.
Levy et al. [9,10] applied feature extraction to paintings using generic image
processing (IP) functions (e.g., fractal dimension, Fourier spectra coefficients, texture coefficients, etc.), and restricted Boltzmann machines (RBM), followed by genetic algorithms (GA)-based learning of the weights of a weighted nearest neighbor
(WNN) classifier [13]. Their approach achieved 90.44% classification accuracy for
the 3-painter classification problem.
In this paper we present the problem of painter classification and briefly survey
recent research that has been conducted in the field. We then present our novel approach, which uses convolutional autoencoders (CAE) instead of image processing
based feature extraction. We subsequently use the trained CAE to initialize a convolutional neural network (CNN) for supervised training on specific painters. The
results demonstrate a substantial improvement over previous methods, improving
the accuracy to 96.52%. This sets a new state-of-the-art for the painter classification
problem,

2

Background

Image authentication is the task of determining whether or not a given painting
was painted by a specific artist. The related task addressed by us, though, is image
classification, i.e., the task of determining the artist of a given painting (from a
certain group of artists). The input to our problem consists of painting images
of the group of artists (several paintings of each artist), and our objective is to
automatically classify a given painting. One of the difficulties in solving this problem
is that we cannot define a certain set of rules that the painting has to conform to
in order to classify it to the subgroup corresponding to the correct artist. For this
reason, computer vision techniques which are capable of identifying shapes and
objects in an image are not sufficiently effective for solving the problem.
Formerly there have been attempts to harness the strength of image analysis
tools to classify historical art paintings into categories of artists or genres. Levy et
al. [9] used GA-based WNN with a set of 78 prevalent image features for classifying paintings by Rembrandt, Renoir, and van Gogh, obtaining 80% classification
accuracy. In their later work [10], they augmented their approach by also adding
20 features using restricted Boltzmann machines (RBM)[2], improving the classification accuracy to 90.44%.
Herik and Postma [15] surveyed image features relevant to the historic art domain and concluded that neural network techniques combined with domain knowledge were most suitable to the task of automatic image classification. Under-drawing
strokes in infrared reflectograms were analyzed by Kammerer et al. [4] in order to
classify how and by what tools paintings are painted. Natural language processing
techniques using a naive-Bayes classifier and the coefficients of a discrete cosine
transform (DCT) were used by Keren [5] in order to classify local features in an
image. Kroner et al. [7] classified drawings by using image histograms and pattern
recognition methods.
The above past research focused on specific image processing features tailored
for specific datasets (such as ink paintings, infrared reflectograms, or black and
white sketches). This domain-specific knowledge facilitates the exploitation of various characteristics of the painting-specific domain.

DeepPainter: Painter Classification Using Deep Convolutional Autoencoders

3

In the next section we present our convolutional autoencoder based approach,
which does not incorporate any domain-specific knowledge, and in fact is operating
solely on the raw pixel level.

3
3.1

Feature Extraction Using Convolutional Autoencoders
Convolutional Neural Networks

In recent years convolutional neural networks (CNN)[1,8,6] have outperformed conventional image processing methods in all computer vision related tasks they have
been applied to. The architecture of a CNN typically includes several components
which are stacked on top of each other: the convolutional layer, the max-pooling
layer, which subsamples the data (e.g., for each 2 × 2 region selects only the maximum value, thus resulting in four times reduction in size), and finally a classification
layer (and usually several fully connected layers before the classification layer). Figure 1 shows a typical CNN,

Fig. 1. Typical architecture of a convolutional neural network.

Standards CNNs are usually used in a supervised framework, where a large
training dataset (typically including at least many thousands of images per class)
is available. Thus, using CNNs for end-to-end painter classification is problematic,
due to a smaller number of training samples available per painter (usually from a
few tens of paintings to at most a few hundred paintings for more prolific painters).
3.2

Autoencoders

Where small number of training samples are available, unsupervised pretraining has
proven highly effective [2,16]. Unsupervised training methods using neural networks
involve either the use of restricted Boltzmann machines (RBM)[2] which are trained
using contrastive divergence, or autoencoders[16] which are training using standard
backpropagation.
The basic principle for all methods involves receiving an input x and mapping
it to a latent representation h, using a function h = σ(W x + b), where σ is a
nonlinear activation function, W is a matrix of weights between the two layers,
and b is bias. The autoencoder then tries to reconstruct the original input by y =
σ(W 0 h + b0 ). Thus, each training sample xi is first mapped to a hidden layer hi
and then reconstructed to yi . The autoencoder is trained using backpropagation to
reduce this reconstruction error.

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3.3

E.O. David, N.S. Netanyahu

Convolutional Autoencoders

The principles behind convolutional neural networks and autoencoders can be combined to produce convolutional autoencoders (CAE). Several approaches involving
the combination of these methods have been explored in the past, and here we use
a CAE architecture along the lines presented in [11,18,17].
In order to use CNNs as autoencoders, for each convolutional layer, a corresponding deconvolutional layer should be constructed. Additionally, max-pooling
layers result in loss of information, and so an unpooling layer should try to approximately restore the original values. Note that the subsampling due to max-pooling
in fact operates as a strong regularizer.
Deconvolution layers can either be equal but transposed to the original convolution layers, or learned from scratch. Often both approaches work equally well in
practice. This is similar to standard autoencoders where the weights of the decoder
layer W 0 can either be learned from scratch, or set to the transpose of the encoder
layer (W 0 = W T ), this is referred to as tied weights.

Fig. 2. Pooling and unpooling layers. For each pooling layer, the max locations are stored.
These locations are then used in unpooling layer.

Several methods have been applied in the past for the unpooling operation
[11,18,17]. Here we employ the method used in [18], where during pooling, the
location of maximum value is stored, such that during unpooling the value is restored in that location, and the other locations are set to zero. Figure 2 illustrates
unpooling, and Figure 3 shows a complete convolutional autoencoder structure.
After training a CAE, we can remove the unpooling and deconvolution components, and use the convolution and pooling components to initialize a supervised
CNN, by adding a fully connected layer followed by a classification layer.

4

CAE and CNN for Painter Classification

For unsupervised training of CAE, we use a randomly selected set of 5,000 paintings
from the Webmuseum (webmuseum.meulie.net/wm). The images have 24-bit color
depth with varying resolutions averaged approximately at 1000 × 1000 pixels, and
compressed as JPEG formatted files. We have resampled the images and normalized
them to 256 × 256 pixels. The goal here is to train the CAE to find features that are

DeepPainter: Painter Classification Using Deep Convolutional Autoencoders

5

specifically useful for paintings, which have a more specific color and composition
range in comparison to real-world images.

Fig. 3. Illustration of convolutional autoencoder. In this example the CAE comprises two
convolution layers and their two corresponding deconvolution layers, and two max-pooling
layers and their corresponding unpooling layers.

Our CAE contains the following layers (see Figure 3). The convolution filter sizes
are always of size 5 × 5.
1. the input layer consists of the raw image (resampled to 256 × 256 pixels) in
three channels (R, G, and B)
2. convolutional layer of size 100 × 256 × 256
3. max-pooling layer of size 2 × 2
4. convolutional layer of size 200 × 128 × 128
5. max-pooling layer of size 2 × 2
6. unpooling layer of size 2 × 2
7. deconvolutional layer of size 200 × 128 × 128
8. unpooling layer of size 2 × 2
9. deconvolutional layer of size 100 × 256 × 256
The learning rate starts from 0.01 and is multiplied by 0.98 after each epoch.
In order to further encourage the CAE to find meaningful features, we randomly
remove 20% of the pixels for the images per epoch. The concept here is similar to
that of denoising autoencoders [16] which outperform traditional autoencoders.
The supervised classification benchmark is identical to that used by Levy et al.
[9,10] in their experiments. It consists of (3×40 =) 120 digital reproductions of paintings by Rembrandt, Renoir, and van Gough, downloaded from the Webmuseum. The
Appendix contains the painting titles of the images used in our experiments.
Having trained a CAE, we can now remove the decoder components (items 6 to
9 in the above list) and use the CAE for initializing a supervised CNN. On top of
these components due to CNN, we add two fully connected layers of size 400 and
200, followed by a softmax output unit of size three (since there are three painters
in the benchmark). The cross entropy loss is used.
The full CNN contains the following layers (see Figure 1):
1. the input layer consists of the raw image (resampled to 256 × 256 pixels) in
three channels (R, G, and B)
2. convolutional layer with 100 5 × 5 filters per input channel
3. max-pooling layer of size 2 × 2
4. convolutional layer with 200 5 × 5 filters per map

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E.O. David, N.S. Netanyahu

5.
6.
7.
8.

max-pooling layer of size 2 × 2
fully connected layer of size 400
fully connected layer of size 200
output softmax layer of size 3

To make our results directly comparable to those of Levy et al. [9,10], we conducted 10-fold cross validation, where in each of 10 runs 90% of the data is used for
training, and 10% for validation.
After performing 10 such training and validation runs, the average accuracy
obtained for our CNN over the validation set is 96.52%. This represents a 63%
reduction in error rate in comparison to the previous state-of-the-art on this benchmark, which stood at 90.44%.
Table 1 provides a summary of the classification accuracies obtained by previous
methods and our method.
Feature extraction method

Supervised learning method

Accuracy

Image Processing
Image Processing
Image Processing
RBM
RBM
RBM
Image Processing + RBM
Image Processing + RBM
Image Processing + RBM
Convolutional Autoencoder

Nearest Neighbor
SVM
Genetic Algorithm
Nearest Neighbor
SVM
Genetic Algorithm
Nearest Neighbor
SVM
Genetic Algorithm
CNN

65.71%
68.33%
78.33%
64.41%
77.50%
73.92%
68.71%
71.66%
90.44%
96.52%

Table 1. Classification accuracy for several previous methods and our CAE based method.
The results are the average over 10-fold cross validation.

5

Conclusion

Automatic painter classification has gained much attention over the past decades,
and much progress has been made with regards to both relevant preprocessing
techniques and classification algorithms. Still, the problem of painter classification
remains a complex task that requires more sophisticated techniques.
The results presented in this paper show that deep learning methods can be
effectively employed for painter classification. Specifically, our results show that
convolutional autoencoders are capable of extracting meaningful information from
paintings, and combined with supervised convolutional networks, we managed to
substantially improve the previous state-of-the-art, from 90.44% accuracy (previous
state-of-the-art) to 96.52% accuracy, i.e., a 63% reduction in error rate.

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This appendix lists the (40 × 3) = 120 titles of the paintings experimented with
by van Gogh, Rembrandt, and Renoir.
#
van Gogh
Rembrandt
Renoir
1
bandaged-ear
abraham
apres-bain
2
berceuse
anslo
baigneuses
3
cordeville
aristotle-homer bathers-1887
4
corridor-asylum
artemis
bathers-1918
5
cypress-star
bathing-river
bougival
6
cypresses
bathsheba
canoeist
7 flower-beds-holland
belshazzar
chocquet
8
green-vineyard
children
city
9 green-wheat-field
danae
country
10 house-ploughman
david
dancer
11
mme-trabuc
descent
durieux
12
mr-trabuc
emmaus
flowers
13
old-mill
hendrickje
gabrielle
14
old-vineyard
holy-family
girl-seated
15
olive-alpilles
jan-six
jugglers
16
olive-trees
magn-glass
lady-piano
17orchard-bloom-poplars
meditation
laundress
18 orchard-plum-trees
mill
loge
19
poppies
music-party
lucie-berard
20
red-vineyard
nicolaes-tulp
near-lake
21
reminiscences
old-man
fournaise
22
road-menders
ostrich
horsewoman
23
roulin
potiphar
meadow
24
self-1
prodigal-son
moulin-galette
25
self-2
raising-lazarus
nini
26
self-easel
.1640
parapluies
27
self-gauguin
.1661
premiere-sortie
28
self-orsay
.1669
promenade
29
self-whitney
.night-watch
ride
30
skull-cigarette
return-prodigal-son romain-lacaux
31
sun-cloud
ruts
sisley-wife
32 threatening-skies
samson
women
33
trees-asylum
scholar
seashore
34 trees-ivy-asylum
self-1629
seated-bather
35
village-stairs
self-1634
sewing
36
wheat-field
self-1660
sisley
37 wheat-rising-sun
slaughtered-ox
swing
38
willows
staalmeesters
terrace
39
peasant
stofells
watercan
40
woman-arles
tobias
woman-veil

