In this paper, we capture and explore the painterly depictions of materials to enable the
study of depiction and perception of materials through the artists' eye. We annotated a …

Deep learning has paved the way for strong recognition systems which are often both
trained on and applied to natural images. In this paper, we examine the give-and-take …

Painters are masters of depiction and have learned to evoke a clear perception of materials
and material attributes in a natural, three-dimensional setting, with complex lighting …

Artistic composition (the structural organization of pictorial elements) is often characterized
by some basic rules and heuristics, but art history does not offer quantitative tools for …

The goal of large-scale automatic paintings analysis is to classify and retrieve images using
machine learning techniques. The traditional methods use computer vision techniques on …

Painters are masters in replicating the visual appearance of materials. While the perception
of material appearance is not yet fully understood, painters seem to have acquired an …

Deep learning has paved the way for strong recognition systems which are often both
trained on and applied to natural images. In this paper, we examine the give-and-take …

We propose a deep learning algorithm that can detect content and discover co-occurring
patterns of the content in fine art paintings. The following intellectual merits are the …

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 …

We present a machine learning system that can quantify fine art paintings with a set of visual
elements and principles of art. This formal analysis is fundamental for understanding art, but …