Scale-invariant feature extraction by VQ-based local image descriptor

Qiu Chen, Koji Kotani, Feifei Lee, Tadahiro Ohmi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publication2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008
Pages1217-1222
Number of pages6
DOIs
Publication statusPublished - 2008 Dec 1
Event2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008 - Vienna, Austria
Duration: 2008 Dec 102008 Dec 12

Publication series

Name2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008

Other

Other2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008
CountryAustria
CityVienna
Period08/12/1008/12/12

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Software
  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'Scale-invariant feature extraction by VQ-based local image descriptor'. Together they form a unique fingerprint.

Cite this