Artistic image analysis using the composition of human figures

Abstract

Artistic image understanding is an interdisciplinary research field of increasing importance for the computer vision and art history communities. One of the goals of this field is the implementation of a system that can automatically retrieve and annotate artistic images. The best approach in the field explores the artistic influence among different artistic images using graph-based learning methodologies that take into consideration appearance and label similarities, but the current state-of the-art results indicate that there seems to be lots of room for improvements in terms of retrieval and annotation accuracy. In order to improve those results, we introduce novel human figure composition features that can compute the similarity between artistic images based on the location and number, i.e., composition, of human figures. Our main motivation for developing such features lies in the importance that composition, particularly the composition of human figures, has in the analysis of artistic images when defining the visual classes present in those images. We show that the introduction of such features in the current dominant methodology of the field improves significantly the state-of-the-art retrieval and annotation accuracies on the PRINTART database, which is a public database exclusively composed of artistic images.