Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Supporting similarity queries in MARS
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Semantics in Visual Information Retrieval
IEEE MultiMedia
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
On Image Classification: City vs. Landscape
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
A Computationally Efficient Approach to Indoor/Outdoor Scene Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Studying digital imagery of ancient paintings by mixtures of stochastic models
IEEE Transactions on Image Processing
Stochastic modeling western paintings for effective classification
Pattern Recognition
Expert Systems with Applications: An International Journal
Analysis of cypriot icon faces using ICA-enhanced active shape model representation
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Hi-index | 0.10 |
Traditional Chinese painting (TCP) is the gem of Chinese traditional arts. More and more TCP images are digitized and exhibited on the Internet. Effectively browsing and retrieving them is an important problem that needs to be addressed. Gongbi (traditional Chinese realistic painting) and Xieyi (freehand style) are two basic types of traditional Chinese paintings. This paper proposes a scheme to detect TCPs from general images and categorize them into Gongbi and Xieyi schools. Low-level features such as color histogram, color coherence vectors, autocorrelation texture features and the newly proposed edge-size histogram are used to achieve the high-level classification. Support vector machine (SVM) is applied as the main classifier to obtain satisfactory classification results. Experimental results show the effectiveness of the method.