Indexing in large scale image collections: Scaling properties and benchmark

Mohamed Aly*, Mario Munich, Pietro Perona

*Corresponding author for this work

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

39 Scopus citations

Abstract

Indexing quickly and accurately in a large collection of images has become an important problem with many applications. Given a query image, the goal is to retrieve matching images in the collection. We compare the structure and properties of seven different methods based on the two leading approaches: voting from matching of local descriptors vs. matching histograms of visual words, including some new methods. We derive theoretical estimates of how the memory and computational cost scale with the number of images in the database. We evaluate these properties empirically on four real-world datasets with different statistics. We discuss the pros and cons of the different methods and suggest promising directions for future research.

Original languageEnglish (US)
Title of host publication2011 IEEE Workshop on Applications of Computer Vision, WACV 2011
Pages418-425
Number of pages8
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 IEEE Workshop on Applications of Computer Vision, WACV 2011 - Kona, HI, United States
Duration: Jan 5 2011Jan 7 2011

Other

Other2011 IEEE Workshop on Applications of Computer Vision, WACV 2011
CountryUnited States
CityKona, HI
Period01/5/1101/7/11

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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