DocumentCode
2665873
Title
Scale-Invariant Feature Extraction by VQ-Based Local Image Descriptor
Author
Chen, Qiu ; Kotani, Koji ; Lee, Feifei ; Ohmi, Tadahiro
Author_Institution
New Ind. Creation Hatchery Center, Tohoku Univ., Sendai, Japan
fYear
2008
fDate
10-12 Dec. 2008
Firstpage
1217
Lastpage
1222
Abstract
SIFT (scale invariant feature transform) feature is identified as being invariant to common image deformations caused by the rotation, scaling, and illumination. In this paper, instead of using SIFT´s smoothed weighted orientation histograms, we apply vector quantization (VQ) histogram as an alternate representation for local image descriptor. Experimental results demonstrate that the VQ-based local descriptors are more robust to image deformations.
Keywords
feature extraction; image coding; vector quantisation; image deformations; local image descriptor; scale invariant feature transform; scale-invariant feature extraction; vector quantization histogram; Electronics industry; Face recognition; Feature extraction; Histograms; Image coding; Industrial electronics; Lighting; Object recognition; Robustness; Vector quantization; Local descriptor; SIFT feature; Vector quantization histogram;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence for Modelling Control & Automation, 2008 International Conference on
Conference_Location
Vienna
Print_ISBN
978-0-7695-3514-2
Type
conf
DOI
10.1109/CIMCA.2008.134
Filename
5172799
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