Iterative quantization: A procrustean approach to learning binary codes

Yunchao Gong, Svetlana Lazebnik

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

Abstract

This paper addresses the problem of learning similarity-preserving binary codes for efficient retrieval in large-scale image collections. We propose a simple and efficient alternating minimization scheme for finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube. This method, dubbed iterative quantization (ITQ), has connections to multi-class spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). Our experiments show that the resulting binary coding schemes decisively outperform several other state-of-the-art methods.

Original languageEnglish (US)
Title of host publication2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
PublisherIEEE Computer Society
Pages817-824
Number of pages8
ISBN (Print)9781457703942
DOIs
StatePublished - Jan 1 2011
Externally publishedYes

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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