Studying digital imagery of ancient paintings by mixtures of stochastic models

This paper addresses learning-based characterization of fine art painting styles. The research has the potential to provide a powerful tool to art historians for studying connections among artists or periods in the history of art. Depending on specific applications, paintings can be categorized in different ways. In this paper, we focus on comparing the painting styles of artists. To profile the style of an artist, a mixture of stochastic models is estimated using training images. The two-dimensional (2-D) multiresolution hidden Markov model (MHMM) is used in the experiment. These models form an artist's distinct digital signature. For certain types of paintings, only strokes provide reliable information to distinguish artists. Chinese ink paintings are a prime example of the above phenomenon; they do not have colors or even tones. The 2-D MHMM analyzes relatively large regions in an image, which in turn makes it more likely to capture properties of the painting strokes. The mixtures of 2-D MHMMs established for artists can be further used to classify paintings and compare paintings or artists. We implemented and tested the system using high-resolution digital photographs of some of China's most renowned artists. Experiments have demonstrated good potential of our approach in automatic analysis of paintings. Our work can be applied to other domains.