Art painting evaluation is sophisticated for a novice with no or limited knowledge on art criticism and history. In this study, we propose the concept of representativity to evaluate paintings instead of using professional concepts, such as genre, media, and style, which may be confusing to non-professionals. We define the concept of representativity to evaluate quantitatively the extent to which a painting can represent the characteristics of an artist's creations. We begin by proposing a novel deep representation of art paintings, which is enhanced by style information through a weighted pooling feature fusion module. In contrast to existing feature extraction approaches, the proposed framework embeds painting styles and authorship information and learns specific artwork characteristics in a single framework. Subsequently, we propose a graph-based learning method for representativity learning, which considers intra-category and extra-category information. In view of the significance of historical factors in the art domain, we introduce the creation time of a painting into the learning process. User studies demonstrate our approach helps the public effectively access the creation characteristics of artists through sorting paintings by representativity from highest to lowest.
@article{Deng2020ExploringRepresentativityArt,
author = {Y. Deng, F. Tang, W. Dong, C. Ma, F. Huang, O. Deussen, C. Xu},
doi = {10.1109/tmm.2020.3016887},
journal = {IEEE Transactions on Multimedia},
pages = {1--1},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
title = {Exploring the Representativity of Art Paintings},
url = {http://graphics.uni-konstanz.de/publikationen/Deng2020ExploringRepresentativityArt},
year = {2020}
}