Abstract
Traditional Chinese painting (“Guohua”) is the gem of Chinese traditional arts. More and more Guohua images are digitized and exhibited on the Internet. Effectively browsing and retrieving them is an important problem need to be addressed. This paper proposes a method to categorize them into Gongbi and Xieyi schools, which are two basic types of traditional Chinese paintings. A new low-level feature called edge-size histogram is proposed and used to achieve such a high level classification. Autocorrelation texture feature is also used. Our method based on SVM classifier achieves a classification accuracy of over 94% on a 3688 traditional Chinese painting database.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Smeulders, A.W.M., Worring, M., Gupta, A., Jain, R.: Content-Based Image Retrieval at the End of the Early Years. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(12) (2000)
Szummer, M., Picard, R.: Indoor-Outdoor Image Classification. In: IEEE International Workshop on Content-based Access of Image and Video Databases, in conjunction with ICCV 1998, Bombay, India (1998)
Serrano, N., Savakis, A., Luo, J.: A Computationally Efficient Approach to Indoor/ Outdoor Scene Classification. In: IEEE ICPR 2002, Québec City, Canada (August 2002)
Vailaya, A., Jain, A., Zhang, H.J.: On Image Classification:City vs. Landscape. In: IEEE Workshop on Content-Based Access of Image and Video Libraries, Santa Barbara, California, June 21 (1998)
Prabhakar, S., Cheng, H., Handley John, C., Fan, Z.G., Lin, Y.W.: Picture-Graphics Color Image Classification. In: IEEE ICIP 2002 (2002)
Wang, J.Z., Li, J., Wiederhold, G., Firschein, O.: Systems for Screening Objectionable Images. Computer Comm. 21(15) (1998)
Fleck, M., Forsyth, D.A., Bregler, C.: Finding Naked People. In: Proc. European Conf. Computer Vision, vol. 2 (1996)
Chen, C.C., Bimbo, A., Amato, G., Boujemaa, N.: Report of the DELOS-NSF Working Group on Digital Imagery for Significant Cultural and Historical Materials. DELOS-NSF Reports (December 2002)
Li, J., Wang, J.Z.: Studying Digital Imagery of Ancient Paintings by Mixtures of Stochastic Models. IEEE Transactions on Image Processing 12(2), 15 (2004)
Leykin, A., Cutzu, F., Hammoud, H.: Visual Properties Differentiating Art from Real Scenes. Technical Report No. 565, Computer Science Department, Indiana University
Shuqiang, J., Wen, G., Weiqiang, W.: Classifying Traditional Chinese Painting Images. In: The 4th International Conference on Information, Communications and Signal Processing - 4th IEEE Pacific-Rim Conference on Multimedia, ICICSPCM 2003 (2003)
Colombo, C., Bimbo, A.D., Pala, P.: Semantics in Visual Information Retrieval. IEEE Multimedia, Los Alamitos (July-September 1999)
International Organization for Standardization Organization International De Normalization ISO/IEC JTC1/SC29/WG11 Coding of Moving Pictures and Audio, ISO/IEC JTC1/SC29/WG11 N4031, Singapore (March 2001)
Seong-O, S., Tae-Sun, C.: Edge Color Histogram for Image Retrieval. In: IEEE ICIP 2002 (2002)
Tuceryan, M., Jain, A.K.: Texture Analysis. In: Chen, C.H., Pau, L.F., Wang, P.S.P. (eds.) The Handbook of Pattern Recognition and Computer Vision, 2nd edn., pp. 207–248. World Scientific Publishing Co., Singapore (1998)
Burges, B.: Tutorial on Support Vector Machine for Pattern Recognition. Data Mining and Knowledge Discovery 2(2) (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jiang, S., Huang, T. (2004). Categorizing Traditional Chinese Painting Images. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30541-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-540-30541-5_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23974-1
Online ISBN: 978-3-540-30541-5
eBook Packages: Computer ScienceComputer Science (R0)