Near-duplicate detection using a new framework of constructing accurate affine invariant regions

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

Abstract

In this study, we propose a simple, yet general and powerful framework for constructing accurate affine invariant regions and use it for near-duplicate detection problem. In our framework, a method for extracting reliable seed points is first proposed. Then, regions which are invariant to most common affine transformations are extracted from seed points by a new method named the Thresholding Seeded Growing Region (TSGR). After that, an improved ellipse fitting method based on the Direct Least Square Fitting (DLSF) is used to fit the irregularly-shaped contours of TSGRs to obtain ellipse regions as the final invariant regions. At last, SIFT-PCA descriptors are computed on the obtained regions. In the experiment, our framework is evaluated by retrieving near-duplicate in an image database containing 1000 images. It gives a satisfying result of 96.8% precision at 100% recall.

Original languageEnglish
Title of host publicationAdvances in Visual Information Systems - 9th International Conference, VISUAL 2007, Revised Selected Papers
PublisherSpringer Verlag
Pages61-72
Number of pages12
ISBN (Print)9783540764137
DOIs
Publication statusPublished - 2007 Jan 1
Event9th International Conference on Visual Information Systems, VISUAL 2007 - Shanghai, China
Duration: 2007 Jun 282007 Jun 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4781 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Visual Information Systems, VISUAL 2007
CountryChina
CityShanghai
Period07/6/2807/6/29

Keywords

  • Ellipse fitting
  • Image matching
  • Invariant region
  • Near-duplicate detection
  • Thresholding seeded growing regions

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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