Iterative quantization: A procrustean approach to learning binary codes

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...

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Bibliographic Details
Published inCVPR 2011 pp. 817 - 824
Main Authors Yunchao Gong, Lazebnik, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2011
Subjects
Binary codes
Encoding
Minimization
Principal component analysis
Quantization
Semantics
Training
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Summary: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.
ISBN:1457703947
9781457703942
ISSN:1063-6919
DOI:10.1109/CVPR.2011.5995432