Dunhuang murals are gems of Chinese traditional art. This paper demonstrates a simple, yet powerful
method to automatically identify the aesthetic visual style that lies in Dunhuang murals. Based on the art
knowledge on Dunhuang murals, the method explicitly predicts some of possible image attributes that a human
might use to understand the aesthetic visual style of a mural. These cues fall into three broad types: composition
attributes related to mural layout or configuration; color attributes related to color types depicted; brightness
attributes related to bright conditions. We show that a classifier trained on these attributes can provide an efficient
way to predict the aesthetic visual style of Dunhuang murals.
YANG Bing1 (杨 冰), XU Duan-qing1* (许端清), TANG Da-wei1 (唐大伟),YANG Xin2 (杨 鑫), ZHAO Lei1 (赵 磊)
. Aesthetic Visual Style Assessment on Dunhuang Murals[J]. Journal of Shanghai Jiaotong University(Science), 2014
, 19(1)
: 28
-34
.
DOI: 10.1007/s12204-014-1473-y
[1] Guo Xiao-li. The enlightenment of Dunhuang fresco color research in Sui-Tang Dynasties to modern color design [D]. Beijing, China: Beijing Institute of Fashion Technology, 2010 (in Chinese).
[2] Huang Jun. The studies for Dunhuang fresco character style in the Tang Dynasty [D]. Beijing, China:China Academy of Art, 2007 (in Chinese).
[3] Keren D. Recognizing image “style” and activities in video using local features and naive Bayes [J]. Pattern Recognition Letters, 2003, 24(16): 2913-2922.
[4] Li J, Wang J Z. Studying digital imagery of ancient paintings by mixtures of stochastic models [J]. IEEE Transactions on Image Processing, 2004, 13(3): 340-353.
[5] Ke Y, Tang X, Jing F. The design of high-level features for photo quality assessment [C]//2006 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR’06). New York, USA: IEEE Computer Society, 2006: 419-426.
[6] Datta R, Joshi D, Li J, et al. Studying aesthetics in photographic images using a computational approach[C]//The 9th European Conference on Computer Vision (ECCV’06). Graz, Austria: Springer, 2006: 288-301.
[7] Moorthy A K, Obrador P, Oliver N. Towards computational models of visual aesthetic appeal of consumer videos [C]//The 11th European Conference on Computer Vision (ECCV’10). Crete, Greece:Springer, 2010: 1-14.
[8] Heihrich W. Principles of art history [M]. Pan Yaochang trans. Shenyang, China: Liaoning People’s Publishing House, 1987.
[9] Liu Xing. Rendering 3D mountain and rock models in Chinese painting style [D]. Shanghai, China: School of Software, Shanghai Jiao Tong University, 2007 (in Chinese).
[10] Wang Xiang-hai, Qin Xiao-bin, Xin Ling. Advances in non-photorealistic rendering [J]. Computer Science,2010, 37(9): 20-27 (in Chinese).
[11] Qian Xiao-yan. Study on non-photorealistic rendering theory and method of artistic style image [D]. Nanjing,China: Nanjing University of Science and Technology,2007 (in Chinese).
[12] Brainard D H. Color appearance and color difference specification [M]. Washington D C, USA: Optical Society of America, 2003: 191-216.
[13] Dhar S, Ordonez V, Berg T L. High level describable attributes for predicting aesthetic and interestingness [C]//2011 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR’11). Colorado Springs, USA: IEEE Computer Society, 2011: 1657-1664.
[14] Gehler P, Nowozin S. On feature combination for multiclass object classification [C]//2009 IEEE International Conference on Computer Vision (ICCV’09).Kyoto, Japan: IEEE, 2009: 221-228.
[15] Parikh D, Grauman K. Relative attributes [C]//2011 IEEE International Conference on Computer Vision (ICCV’11). Barcelona, Spain: IEEE,2011: 503-510.
