Scaling object recognition: Benchmark of current state of the art techniques

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

*Corresponding author for this work

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

16 Scopus citations

Abstract

Scaling from hundreds to millions of objects is the next challenge in visual recognition. We investigate and benchmark the scalability properties (memory requirements, runtime, recognition performance) of the state-of-the-art object recognition techniques: the forest of k-d trees, the locality sensitive hashing (LSH) method, and the approximate clustering procedure with the tf-idf inverted index. The characterization of the images was performed with SIFT features. We conduct experiments on two new datasets of more than 100,000 images each, and quantify the performance using artificial and natural deformations. We analyze the results and point out the pitfalls of each of the compared methodologies suggesting potential new research avenues for the field.

Original languageEnglish (US)
Title of host publication2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
Pages2117-2124
Number of pages8
DOIs
StatePublished - Dec 1 2009
Event2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 - Kyoto, Japan
Duration: Sep 27 2009Oct 4 2009

Publication series

Name2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

Conference

Conference2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
CountryJapan
CityKyoto
Period09/27/0910/4/09

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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