Studying Digital Imagery of Ancient Paintings
by Mixtures of Stochastic Models
Jia Li, James Z. Wang
The Pennsylvania State University, University Park, PA 16802
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 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.
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The copyrights of the images of ancient works belong to the museums or
the owners of the images. Some images are used in the project under
teaching, scholarship or research, which are considered as "Fair Use"
because (1) the amount of copyrighted work used is reasonable, (2) the
importance of that part of the work is not substantial to the whole work, and
(3) the effect of the use upon the value or potential value of the
copyrighted work is not significant. (See 17 U.S.C.A. § 107).
Jia Li and James Z. Wang, ``Studying Digital Imagery of Ancient
Paintings by Mixtures of Stochastic Models,'' IEEE Transactions on
Image Processing, vol. 13, no. 3, pp. 340-353, 2004.
Copyright 2003 IEEE.
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October 8, 2003