Towards scalable dataset construction: An active learning approach

Brendan Collins, Jia Deng, Kai Li, Li Fei-Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

54 Scopus citations


As computer vision research considers more object categories and greater variation within object categories, it is clear that larger and more exhaustive datasets are necessary. However, the process of collecting such datasets is laborious and monotonous. We consider the setting in which many images have been automatically collected for a visual category (typically by automatic internet search), and we must separate relevant images from noise. We present a discriminative learning process which employs active, online learning to quickly classify many images with minimal user input. The principle advantage of this work over previous endeavors is its scalability. We demonstrate precision which is often superior to the state-of-the-art, with scalability which exceeds previous work.

Original languageEnglish (US)
Title of host publicationComputer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings
PublisherSpringer Verlag
Number of pages13
EditionPART 1
ISBN (Print)3540886818, 9783540886815
StatePublished - 2008
Event10th European Conference on Computer Vision, ECCV 2008 - Marseille, France
Duration: Oct 12 2008Oct 18 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5302 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th European Conference on Computer Vision, ECCV 2008

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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