S-SIFT: A shorter SIFT without least discriminability visual orientation

Sheng Hua Zhong, Yan Liu, Gangshan Wu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationProceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012
Pages669-672
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2012
Event2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 - Macau, China
Duration: 4 Dec 20127 Dec 2012

Conference

Conference2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012
CountryChina
CityMacau
Period4/12/127/12/12

Keywords

  • descriptors
  • real-world distribution
  • scale-invariant feature transform
  • visual orientation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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