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https://hdl.handle.net/2440/82580
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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) DICTA |
School/Discipline: | School of Computer Science |
Statement of Responsibility: | 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 |
Description (link): | http://www.aprs.org.au/dicta13/ |
Appears in Collections: | Computer Science publications |
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