Image retrieval and classification using local distance functions

Andrea Frome, Yoram Singer, Jitendra Malik

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

104 Scopus citations

Abstract

In this paper we introduce and experiment with a framework for learning local perceptual distance functions for visual recognition. We learn a distance function for each training image as a combination of elementary distances between patch-based visual features. We apply these combined local distance functions to the tasks of image retrieval and classification of novel images. On the Caltech 101 object recognition benchmark, we achieve 60.3% mean recognition across classes using 15 training images per class, which is better than the best published performance by Zhang, et al.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference
Pages417-424
Number of pages8
StatePublished - Dec 1 2007
Externally publishedYes
Event20th Annual Conference on Neural Information Processing Systems, NIPS 2006 - Vancouver, BC, Canada
Duration: Dec 4 2006Dec 7 2006

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other20th Annual Conference on Neural Information Processing Systems, NIPS 2006
CountryCanada
CityVancouver, BC
Period12/4/0612/7/06

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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  • Cite this

    Frome, A., Singer, Y., & Malik, J. (2007). Image retrieval and classification using local distance functions. In Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference (pp. 417-424). (Advances in Neural Information Processing Systems).