Translation-invariant scene grouping

Pin Ching Su, Hwann Tzong Chen, Koichi Ito, Takafumi Aoki

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


We present a new approach to the problem of grouping similar scene images. The proposed method characterizes both the global feature layout and the local oriented edge responses of an image, and provides a translation-invariant similarity measure to compare scene images. Our method is effective in generating initial clustering results for applications that require extensive local-feature matching on unorganized image collections, such as large-scale 3D reconstruction and scene completion. The advantage of our method is that it can estimate image similarity via integrating global and local information. The experimental evaluations on various image datasets show that our method is able to approximate well the similarities derived from local-feature matching with a lower computational cost.

Original languageEnglish
Title of host publication1st Asian Conference on Pattern Recognition, ACPR 2011
Number of pages5
Publication statusPublished - 2011
Event1st Asian Conference on Pattern Recognition, ACPR 2011 - Beijing, China
Duration: 2011 Nov 282011 Nov 28

Publication series

Name1st Asian Conference on Pattern Recognition, ACPR 2011


Other1st Asian Conference on Pattern Recognition, ACPR 2011


  • Gist Descriptor
  • Image Matching
  • Phase-Only Correlation
  • SIFT Descriptor
  • Scene Clustering

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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