This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval
Dec. 2013 (vol. 35 no. 12)
pp. 2916-2929
Yunchao Gong, Dept. of Comput. Sci., Univ. of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Svetlana Lazebnik, Dept. of Comput. Sci., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA
Albert Gordo, Comput. Vision Center, Univ. Autonoma de Barcelona, Barcelona, Spain
Florent Perronnin, Textual Visual Pattern Anal., Xerox Res. Centre Eur., Meylan, France
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.
Index Terms:
iterative methods,binary codes,image coding,image retrieval,Procrustean approach,ImageNet data set,classemes,learning binary attributes,nonlinear kernel mapping,CCA,canonical correlation analysis,supervised embeddings,PCA,unsupervised data embeddings,orthogonal Procrustes problem,spectral clustering,ITQ,dubbed iterative quantization,minimization algorithm,zero centered binary hypercube,quantization error,zero centered data,large scale image collections,similarity search,similarity preserving binary codes learning,large scale image retrieval,Quantization,Binary codes,Principal component analysis,Encoding,Linear programming,Iterative methods,quantization,Large-scale image search,binary codes,hashing
Citation:
Yunchao Gong, Svetlana Lazebnik, Albert Gordo, Florent Perronnin, "Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 12, pp. 2916-2929, Dec. 2013, doi:10.1109/TPAMI.2012.193
Usage of this product signifies your acceptance of the Terms of Use.