PT Journal
AU Yunchao Gong
   Svetlana Lazebnik
   Albert Gordo
   Florent Perronnin
TI Iterative quantization: A procrustean approach to learning binary codes for Large-Scale Image Retrieval
SO IEEE Transactions on  Pattern Analysis and Machine Intelligence
JI TPAMI
PY 2012
BP 2916
EP 2929
VL 35
IS 12
DI 10.1109/TPAMI.2012.193
AB 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 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). 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 dataset.
ER