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KPB-SIFT: a compact local feature descriptor

Published: 25 October 2010 Publication History

Abstract

Invariant feature descriptors such as SIFT and GLOH have been demonstrated to be very robust for image matching and object recognition. However, such descriptors are typically of high dimensionality, e.g. 128-dimension in the case of SIFT. This limits the performance of feature matching techniques in terms of speed and scalability. A new compact feature descriptor, called Kernel Projection Based SIFT (KPB-SIFT), is presented in this paper. Like SIFT, our descriptor encodes the salient aspects of image information in the feature point's neighborhood. However, instead of using SIFT's smoothed weighted histograms, we apply kernel projection techniques to orientation gradient patches. The produced KPB-SIFT descriptor is more compact as compared to the state-of-the-art, does not require pre-training step needed by PCA based descriptors, and shows superior advantages in terms of distinctiveness, invariance to scale, and tolerance of geometric distortions. We extensively evaluated the effectiveness of KPB-SIFT with datasets acquired under varying circumstances.

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http://www.robots.ox.ac.uk/~vgg/research/affine/

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  • (2017)Learning moment-based fast local binary descriptorJournal of Electronic Imaging10.1117/1.JEI.26.2.02300626:2(023006)Online publication date: 16-Mar-2017
  • (2017)Object classification using a local texture descriptor and a support vector machineMultimedia Tools and Applications10.1007/s11042-016-4003-076:20(20609-20641)Online publication date: 1-Oct-2017
  • (2016)Image Registration Techniques Based on the Scale Invariant Feature TransformIETE Technical Review10.1080/02564602.2016.114107634:1(22-29)Online publication date: 18-Feb-2016
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cover image ACM Conferences
MM '10: Proceedings of the 18th ACM international conference on Multimedia
October 2010
1836 pages
ISBN:9781605589336
DOI:10.1145/1873951
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 October 2010

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Author Tags

  1. SIFT
  2. descriptor
  3. feature
  4. kernel projection

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  • Short-paper

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MM '10
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MM '10: ACM Multimedia Conference
October 25 - 29, 2010
Firenze, Italy

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

View all
  • (2017)Learning moment-based fast local binary descriptorJournal of Electronic Imaging10.1117/1.JEI.26.2.02300626:2(023006)Online publication date: 16-Mar-2017
  • (2017)Object classification using a local texture descriptor and a support vector machineMultimedia Tools and Applications10.1007/s11042-016-4003-076:20(20609-20641)Online publication date: 1-Oct-2017
  • (2016)Image Registration Techniques Based on the Scale Invariant Feature TransformIETE Technical Review10.1080/02564602.2016.114107634:1(22-29)Online publication date: 18-Feb-2016
  • (2016)Face Recognition using Filtered Eoh-siftProcedia Computer Science10.1016/j.procs.2016.03.06979(543-552)Online publication date: 2016
  • (2015)Scan without a GlanceProceedings of the 2015 44th International Conference on Parallel Processing (ICPP)10.1109/ICPP.2015.34(250-259)Online publication date: 1-Sep-2015
  • (2015)MDGHM-SURFPattern Recognition10.1016/j.patcog.2014.06.02248:3(670-684)Online publication date: 1-Mar-2015
  • (2015)Regularized discriminant embedding for visual descriptor learningNeurocomputing10.1016/j.neucom.2014.07.029149:PB(1048-1057)Online publication date: 3-Feb-2015
  • (2014)Feature description based on center-symmetric local mapped patternsProceedings of the 29th Annual ACM Symposium on Applied Computing10.1145/2554850.2554895(39-44)Online publication date: 24-Mar-2014
  • (2014)Coding Visual Features Extracted From Video SequencesIEEE Transactions on Image Processing10.1109/TIP.2014.231261723:5(2262-2276)Online publication date: May-2014
  • (2014)BSIFT: Boosting SIFT using principal component analysis2014 22nd Iranian Conference on Electrical Engineering (ICEE)10.1109/IranianCEE.2014.6999705(1130-1135)Online publication date: May-2014
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