Towards automated large scale discovery of image families

Mohamed Aly*, Peter Welinder, Mario Munich, Pietro Perona

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

Abstract

Gathering large collections of images is quite easy nowadays with the advent of image sharing websites, such as flickr. com. However, such collections inevitably contain duplicates and highly similar images, what we refer to as image families. Automatic discovery and cataloguing of such similar images in large collections is important for many applications, e.g. image search, image collection visualization, and research purposes among others. In this work, we investigate this problem by thoroughly comparing two broad approaches for measuring image similarity: global vs. local features. We assess their performance as the image collection scales up to over 11,000 images with over 6,300 families. We present our results on three datasets with different statistics, including two new challenging datasets. Moreover, we present a new algorithm to automatically determine the number of families in the collection with promising results.

Original languageEnglish (US)
Title of host publication2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009
Pages9-16
Number of pages8
DOIs
StatePublished - Nov 20 2009
Event2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009 - Miami, FL, United States
Duration: Jun 20 2009Jun 25 2009

Publication series

Name2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Other

Other2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009
CountryUnited States
CityMiami, FL
Period06/20/0906/25/09

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

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