TY - JOUR
T1 - Applying multi-class SVMs into scene image classification
AU - Ren, Jianfeng
AU - Shen, Yuntao
AU - Ma, Songhui
AU - Guo, Lei
PY - 2004
Y1 - 2004
N2 - Grouping images into semantically meaningful categories using the low-level visual features is a challenging and important problem in content-based image retrieval and other applications. In this paper, we show a specific high-level classification problem (scene images classification) using the low level features such as representative colors and Gabor textures. Based on the low level features, we introduce the multi-class SVMs to merge these features with the final goal to classify the different scene images. Experimental results show our method is promising.
AB - Grouping images into semantically meaningful categories using the low-level visual features is a challenging and important problem in content-based image retrieval and other applications. In this paper, we show a specific high-level classification problem (scene images classification) using the low level features such as representative colors and Gabor textures. Based on the low level features, we introduce the multi-class SVMs to merge these features with the final goal to classify the different scene images. Experimental results show our method is promising.
UR - http://www.scopus.com/inward/record.url?scp=9644290929&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-24677-0_95
DO - 10.1007/978-3-540-24677-0_95
M3 - 会议文章
AN - SCOPUS:9644290929
SN - 0302-9743
VL - 3029
SP - 924
EP - 934
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004
Y2 - 17 May 2004 through 20 May 2004
ER -