TY - JOUR
T1 - Studying digital imagery of ancient paintings by mixtures of stochastic models
AU - Li, Jia
AU - Wang, James Z.
N1 - Funding Information:
Manuscript received February 16, 2003; revised October 23, 2003. This work was supported by the U.S. National Science Foundation under Grant IIS-0219272, The Pennsylvania State University, the PNC Foundation, and Sun Microsystems under Grant EDUD-7824-010456-US. The Website http://wang.ist.psu.edu provides more information related to this work. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Ioannis Pitas.
PY - 2004/3
Y1 - 2004/3
N2 - 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.
AB - 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.
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U2 - 10.1109/TIP.2003.821349
DO - 10.1109/TIP.2003.821349
M3 - Article
C2 - 15376926
AN - SCOPUS:1942517858
SN - 1057-7149
VL - 13
SP - 340
EP - 353
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 3
ER -