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Proceedings Paper

Content based image retrieval using extended Gaussian Lie group spatiogram similarity
Author(s): Feng Cheng; Xiaqiong Yu; Zuxi Wang; Dehua Li
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Paper Abstract

The spatiogram features have been widely used in computer vision. In this paper, in order to improve the performance of image retrieval method based on spatiogram features, we propose a new method to measure the spatiogram similarity in the framework of extended Gaussian Lie group model. In our method, the spatiogram features are extracted in the HSV space. The similarity between images described by spatiogram features depends on the distances between Gaussian probability density functions which can be calculated using Lie group theory. Based on the framework of the extended Gaussian matrix Lie group, the contribution of the covariance matrix and the mean vector is adjusted automatically, which ensures both the covariance matrix and the mean vector will not be ignored when calculating the image similarity in the process of retrieval. We test our algorithm on the WANG Image Database. Experiments show that the proposed method has a better performance than the method based on the traditional Gaussian Lie group.

Paper Details

Date Published: 19 July 2013
PDF: 5 pages
Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 887812 (19 July 2013); doi: 10.1117/12.2030728
Show Author Affiliations
Feng Cheng, Huazhong Univ. of Science and Technology (China)
Xiaqiong Yu, TH Satellite Ctr. of China (China)
Zuxi Wang, Huazhong Univ. of Science and Technology (China)
Dehua Li, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 8878:
Fifth International Conference on Digital Image Processing (ICDIP 2013)
Yulin Wang; Xie Yi, Editor(s)

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