Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval

Yunchao Gong, Svetlana Lazebnik, Albert Gordo, Florent Perronnin

Research output: Contribution to journalArticlepeer-review


This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of 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, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multiclass 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). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or 'classemes' on the ImageNet data set.

Original languageEnglish (US)
Article number6296665
Pages (from-to)2916-2929
Number of pages14
JournalIEEE transactions on pattern analysis and machine intelligence
Issue number12
StatePublished - 2013


  • Large-scale image search
  • binary codes
  • hashing
  • quantization

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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