Quantifying the specificity of near-duplicate image classification functions

Richard Connor, Franco Alberto Cardillo

Research output: Contribution to conferencePaper

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Abstract

There are many published methods for detecting similar and near-duplicate images. Here, we consider their use in the context of unsupervised near-duplicate detection, where the task is to find a (relatively small) near-duplicate intersection of two large candidate sets. Such scenarios are of particular importance in forensic near-duplicate detection. The essential properties of a such a function are: performance, sensitivity, and specificity. We show that, as collection sizes increase, then specificity becomes the most important of these, as without very high specificity huge numbers of false positive matches will be identified. This makes even very fast, highly sensitive methods completely useless. Until now, to our knowledge, no attempt has been made to measure the specificity of near-duplicate finders, or even to compare them with each other. Recently, a benchmark set of near-duplicate images has been established which allows such assessment by giving a near-duplicate ground truth over a large general image collection. Using this we establish a methodology for calculating specificity. A number of the most likely candidate functions are compared with each other and accurate measurement of sensitivity vs. specificity are given. We believe these are the first such figures be to calculated for any such function.
Original languageEnglish
Number of pages8
Publication statusPublished - 27 Feb 2016
Event11th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Rome, Italy
Duration: 27 Feb 201629 Feb 2016

Conference

Conference11th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
CountryItaly
CityRome
Period27/02/1629/02/16

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Keywords

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

Cite this

Connor, R., & Cardillo, F. A. (2016). Quantifying the specificity of near-duplicate image classification functions. Paper presented at 11th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Rome, Italy.