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Optical Engineering

Unsupervised clustering for logo images using singular values region covariance matrices on Lie groups
Author(s): Xuguang Zhang; Yun Zhang; Jie Zhang; Xiaoli Li; Shengyong Chen; Dan Chen
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Paper Abstract

Toward the unsupervised clustering for color logo images corrupted by noise, we propose a novel framework in which the logo images are described by a model called singular values based region covariance matrices (SVRCM), and the mean shift algorithm is performed on Lie groups for clustering covariance matrices. To decrease the influence of noise, we choose the larger singular values, which can better represent the original image and discard the smaller singular values. Therefore, the chosen singular values are grouped and fused by a covariance matrix to form a SVRCM model that can represent the correlation and variance between different singular value features to enhance the discriminating ability of the model. In order to cluster covariance matrices, which do not lie on Euclidean space, the mean shift algorithm is performed on manifolds by iteratively transforming points between the Lie group and Lie algebra. Experimental results on 38 categories of logo images demonstrate the superior performance of the proposed method whose clustering rate can be achieved at 88.55%.

Paper Details

Date Published: 19 April 2012
PDF: 9 pages
Opt. Eng. 51(4) 047005 doi: 10.1117/1.OE.51.4.047005
Published in: Optical Engineering Volume 51, Issue 4
Show Author Affiliations
Xuguang Zhang, Yanshan Univ. (China)
Yun Zhang, Yanshan Univ. (China)
Jie Zhang, Yanshan Univ. (China)
Xiaoli Li, Yanshan Univ. (China)
Shengyong Chen, Zhejiang Univ. of Technology (China)
Dan Chen, China Univ. of Geosciences (China)

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