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Type: Conference paper
Title: Scale-less feature-spatial matching
Author: Zhang, Chao
Shen, Tingzhi
Citation: 2013 International Conference on Digital Image Computing: Techniques and Applications, DICTA, Hobart, Tasmania, 26-28 November 2013: 8 p.
Publisher: IEEE
Issue Date: 2013
ISBN: 9781479921263
Conference Name: International Conference on Digital Image Computing: Techniques and Applications (2013 : Hobart, Tasmania)
School/Discipline: School of Computer Science
Statement of
Chao Zhang, Tingzhi Shen
Abstract: In this paper, we improve the discriminability of the Scale- Less SIFT (SLS) descriptor, which is constructed without requiring scale estimation of interest points. We thereby avoid to find stable scales which are difficult to obtain in many cases. Scale-Less SIFT descriptors of interest points are represented as sets of SIFT descriptors at multiple scales. We construct the linear subspace as the geometric representation for sets of SIFT descriptors. Then an embedding representation is learned that combines the descriptor similarity across scales and the spatial arrangement in a unified Euclidean embedding space. The learned subspace are highly capable of capturing the scale-varying values of SIFT descriptors. Experiment results demonstrate significant improvements by our constructed descriptors over existing methods on standard benchmark datasets.
Rights: Copyright © 2013 by the Institute of Electrical and Electronic Engineers
DOI: 10.1109/DICTA.2013.6691526
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Appears in Collections:Computer Science publications

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