Identification of MIR-Flickr near-duplicate images: a benchmark collection for near-duplicate detection

Richard Connor, Stewart MacKenzie-Leigh, Franco Alberto Cardillo, Robert Moss

Research output: Contribution to conferencePaper

2 Citations (Scopus)
201 Downloads (Pure)

Abstract

There are many contexts where the automated detection of near-duplicate images is important, for example the detection of copyright infringement or images of child abuse. There are many published methods for the detection of similar and near-duplicate images; however it is still uncommon for methods to be objectively compared with each other, probably because of a lack of any good framework in which to do so. Published sets of near-duplicate images exist, but are typically small, specialist, or generated. Here, we give a new test set based on a large, serendipitously selected collection of high quality images. Having observed that the MIR- Flickr 1M image set contains a significant number of near-duplicate images, we have discovered the majority of these. We disclose a set of 1,958 near-duplicate clusters from within the set, and show that this is very likely to contain almost all of the near-duplicate pairs that exist. The main contribution of this publication is the identification of these images, which may then be used by other authors to make comparisons as they see fit. In particular however, near-duplicate classification functions may now be accurately tested for sensitivity and specificity over a general collection of images.
Original languageEnglish
Pages565-571
Number of pages7
DOIs
Publication statusPublished - 14 Mar 2015
Event10th International Conference on Computer Vision Theory and Applications (VISAPP 2015) - Berlin, Germany
Duration: 11 Mar 201514 Mar 2015

Conference

Conference10th International Conference on Computer Vision Theory and Applications (VISAPP 2015)
CountryGermany
CityBerlin
Period11/03/1514/03/15

Keywords

  • near-duplicate image detection
  • benchmark
  • forensic image detection
  • image similarity function

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