| dc.description.abstract | Being able to classify paintings by painter, style or genre is something most art historians
can do fairly accurately. Recently attempts have been made to do this with
machine learning classifiers. This paper provides a look at the state of the art of
classifying paintings using machine learning classifiers and feature sets. State of the
art classifiers and feature sets will be discussed for various classification approaches.
Per classification approach I discuss applications, benefits, drawbacks, practical usefulness
and performance. Analysis of performance is based on results from literature.
From all of this I conclude that feature sets specifically made for painting classification
should be created for classifying by painter. Based on current literature it seems that
a feature set with features created by a convolutional neural network (CNN) could
work well for classifying by style. For classifiers, it seems that researchers often use
the support vector machine classifier without testing performance of other classifiers.
More research should be done on the performance of classifiers other than support
vector machines on the subject of painting classification. Other approaches and their
problems are also discussed briefly. | |
