Classifying traditional Chinese painting images

Author

Jiang, Shuqiang ; Gao, Wen ; Wang, Weiqiang

Author_Institution

Inst. of Comput. Technol., Chinese Acad. of Sci., Beijing, China

Volume

3

fYear

2003

fDate

15-18 Dec. 2003

Firstpage

1816

Abstract

More and more traditional Chinese painting art images are digitalized and exhibited on the Internet. Effective browsing and retrieving them is an imperative problem need to be addressed. This paper proposes a scheme to classify traditional Chinese paintings. The algorithm uses three low-level features to achieve such a high-level classification: Ohta histogram, color coherence vector and auto-correlation. An accuracy of 97.21% was achieved on the database of 1254 Chinese painting database.

Keywords

Internet; art; image classification; image retrieval; visual databases; Chinese painting images; Internet; Ohta histogram; auto-correlation; browsing; color coherence vector; digitalized images; high-level classification; low-level features; painting database; retrieving; Art; Histograms; Image databases; Image retrieval; Internet; Painting; Spatial databases; Support vector machine classification; Support vector machines; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Information, Communications and Signal Processing, 2003 and Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint Conference of the Fourth International Conference on

Print_ISBN

0-7803-8185-8

Type

conf

DOI

10.1109/ICICS.2003.1292780

Filename

1292780