Feature classification for representative photo selection

Wei Ta Chu, Chia Hung Lin, Jen Yu Yu

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

5 Citations (Scopus)

Abstract

This paper points out that different local feature points provide different impacts to near-duplicate detection and related applications. Aiming to automatic representative photo selection, we develop three feature classification methods, i.e., point-based, region-based, and pLSA-based classification, to differentiate local feature points described by SIFT descriptors. We investigate the performance of these classification methods, and discuss how they influence near-duplicate detection and extended applications. Experiments show that, with effective feature classification, more accurate representative selection results can be achieved.

Original languageEnglish
Title of host publicationMM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums
Pages509-512
Number of pages4
DOIs
Publication statusPublished - 2009 Dec 28
Event17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums - Beijing, China
Duration: 2009 Oct 192009 Oct 24

Publication series

NameMM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums

Conference

Conference17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums
CountryChina
CityBeijing
Period09-10-1909-10-24

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software

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