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Proceedings Paper

Application of multi-class SVM for Kansei landscape image retrieval using colour and Kansei factors
Author(s): Bin Shen; Min Yao; Yan-Gu Zhang; Wen-Sheng Yi
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Paper Abstract

In this paper, a Kansei landscape image retrieval system named KIRCK is proposed, which is based on color feature and Kansei factors. Color feature is extracted in HSV color space and the similarity of color feature is estimated by color accumulation histogram intersection method. Multi-class Support Vector Machine is applied for the mapping between high-level Kansei labels and low-level image characteristics. After the multi-class SVM is trained, Kansei factors of images can be labeled automatically, and the similarity of images in Kansei space also can be estimated. Thus integrated retrieval results using color and Kansei factors can be obtained, and the experiment shows that these retrieval results are more satisfied than only using color feature or Kansei factors. Correlative feedback is also introduced to improve the performance of our color feature and Kansei factors image retrieval.

Paper Details

Date Published: 3 November 2005
PDF: 9 pages
Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 604328 (3 November 2005); doi: 10.1117/12.654989
Show Author Affiliations
Bin Shen, Zhejiang Univ. (China)
Min Yao, Zhejiang Univ. (China)
Nanjing Univ. (China)
Yan-Gu Zhang, Wenzhou Normal College (China)
Wen-Sheng Yi, Zhejiang Univ. (China)

Published in SPIE Proceedings Vol. 6043:
MIPPR 2005: SAR and Multispectral Image Processing
Liangpei Zhang; Jianqing Zhang; Mingsheng Liao, Editor(s)

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