  To facilitate computer analysis of visual art, in the form of paintings, we
introduce Pandora (Paintings Dataset for Recognizing the Art movement)
database, a collection of digitized paintings labelled with respect to the
artistic movement. Noting that the set of databases available as benchmarks for
evaluation is highly reduced and most existing ones are limited in variability
and number of images, we propose a novel large scale dataset of digital
paintings. The database consists of more than 7700 images from 12 art
movements. Each genre is illustrated by a number of images varying from 250 to
nearly 1000. We investigate how local and global features and classification
systems are able to recognize the art movement. Our experimental results
suggest that accurate recognition is achievable by a combination of various
categories.To facilitate computer analysis of visual art, in the form of
paintings, we introduce Pandora (Paintings Dataset for Recognizing the Art
movement) database, a collection of digitized paintings labelled with respect
to the artistic movement. Noting that the set of databases available as
benchmarks for evaluation is highly reduced and most existing ones are limited
in variability and number of images, we propose a novel large scale dataset of
digital paintings. The database consists of more than 7700 images from 12 art
movements. Each genre is illustrated by a number of images varying from 250 to
nearly 1000. We investigate how local and global features and classification
systems are able to recognize the art movement. Our experimental results
suggest that accurate recognition is achievable by a combination of various
categories.
