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Optical Engineering

Extended dot product representations of graphs with application to radar image segmentation
Author(s): Daming Zhang; Dengdi Sun; Maosheng Fu; Bin Luo
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Paper Abstract

Graph-based dimensionality reduction methods are popular in pattern recognition and machine learning. In contrast to the manifold learning approaches, the dot product representation of graphs (DPRG) seeks a solution to dimensionality reduction by assigning vectors to each node of a graph such that the dot product of every pair of nodes approximates the similarity between them. The DPRG has many potential applications, for the reason that there is no prior assumption of the data distribution. It has been found, however, that the DPRG tends to reduce the distances of the graph nodes represented in a low-dimensional space, which in turn degrades the performance of data clustering. Motivated by this observation, we propose an extended DPRG (EDPRG) model by simply employing negative similarity values. The theoretical analysis and experiments on synthetic data show that the modification is effective in increasing between-class distances. We demonstrate the effectiveness of the EDPRG model by experiments on synthetic aperture radar (SAR) image segmentation. The proposed image segmentation method has two steps. The first one presegments the image by the mean shift algorithm. The second merges the resulting regions by means of the EDPRG model.

Paper Details

Date Published: 1 November 2010
PDF: 10 pages
Opt. Eng. 49(11) 117201 doi: 10.1117/1.3505865
Published in: Optical Engineering Volume 49, Issue 11
Show Author Affiliations
Daming Zhang, Anhui Univ. (China)
Dengdi Sun, Anhui Univ. (China)
Maosheng Fu, Anhui Univ. (China)
Bin Luo, Anhui Univ. (China)


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