Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16070
Title: S-SIFT : a shorter SIFT without least discriminability visual orientation
Authors: Zhong, S
Liu, Y 
Wu, G
Keywords: Descriptors
Real-world distribution
Scale-invariant feature transform
Visual orientation
Issue Date: 2012
Publisher: IEEE
Source: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), 4-7 December 2012, Macau, p. 669-672 How to cite?
Abstract: Detection and description of local features are a classical problem in image processing and multimedia content analysis. Based on the in homogeneity of visual orientation in human visual system, we propose a novel algorithm S-SIFT to detect and describe local image features. In three stages of S-SIFT, the information from the least discriminability orientation is omitting. Compared with the standard SIFT algorithm, S-SIFT has lower dimension and provides a faster key point matching. Experiments on the standard dataset demonstrate that our algorithm yields comparable or even better results for feature detection and matching tasks.
URI: http://hdl.handle.net/10397/16070
ISBN: 978-1-4673-6057-9
DOI: 10.1109/WI-IAT.2012.134
Appears in Collections:Conference Paper

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