Bag of words for large scale object recognition: Properties and benchmark

Mohamed Aly*, Mario Munich, Pietro Perona

*Corresponding author for this work

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

9 Scopus citations

Abstract

Object Recognition in a large scale collection of images has become an important application of widespread use. In this setting, the goal is to find the matching image in the collection given a probe image containing the same object. In this work we explore the different possible parameters of the bag of words (BoW) approach in terms of their recognition performance and computational cost. We make the following contributions: 1) we provide a comprehensive benchmark of the two leading methods for BoW: inverted file and min-hash; and 2) we explore the effect of the different parameters on their recognition performance and run time, using four diverse real world datasets.

Original languageEnglish (US)
Title of host publicationVISAPP 2011 - Proceedings of the International Conference on Computer Vision Theory and Application
Pages299-306
Number of pages8
StatePublished - 2011
Externally publishedYes
EventInternational Conference on Computer Vision Theory and Application, VISAPP 2011 - Vilamoura, Algarve, Portugal
Duration: Mar 5 2011Mar 7 2011

Other

OtherInternational Conference on Computer Vision Theory and Application, VISAPP 2011
CountryPortugal
CityVilamoura, Algarve
Period03/5/1103/7/11

Keywords

  • Bag of words
  • Benchmark
  • Image retrieval
  • Image search
  • Inverted file
  • Min hash
  • Object recognition

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this