CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.12 - Dec.
Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval
Issue No.12 - Dec. (2013 vol.35)
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.
Quantization, Binary codes, Principal component analysis, Encoding, Linear programming, Iterative methods,quantization, Large-scale image search, binary codes, hashing
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 & Machine Intelligence, vol.35, no. 12, pp. 2916-2929, Dec. 2013, doi:10.1109/TPAMI.2012.193