Iterative
Quantization: A Procrustean Approach to Learning Binary Codes
Yunchao Gong and
Svetlana Lazebnik
Department of Computer Science, University of North Carolina at Chapel Hill
CVPR 2011
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Abstract:
This paper addresses the problem of
learning similarity preserving binary codes for efficient similarity search
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. |
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Paper:
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Data:
Labeled
CIFAR here Noisy
CIFAR indexes in
the tiny image dataset * data is kindly provided by Rob
Fergus Code
Implements
the ITQ method and several other baselines. |
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