Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval

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 vertic...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 35; no. 12; pp. 2916 - 2929
Main Authors:	Yunchao Gong, Lazebnik, Svetlana, Gordo, Albert, Perronnin, Florent
Format:	Journal Article
Language:	English
Published:	Los Alamitos, CA IEEE 01.12.2013 IEEE Computer Society
Subjects:	Applied sciences Artificial intelligence Binary codes Computer science; control theory; systems Computer systems and distributed systems. User interface Data processing. List processing. Character string processing Encoding Exact sciences and technology hashing Information systems. Data bases Iterative methods Large-scale image search Linear programming Memory organisation. Data processing Pattern recognition. Digital image processing. Computational geometry Principal component analysis Quantization Software Cluster analysis Input output Error estimation Very large databases Iterative method Data center Binary code Canonical correlation Signal quantization Large-scale image search quantization Nearest neighbour Data analysis Spectral method binary codes Quantization error Image retrieval Image databank Minimization Information retrieval Kernel function Hypercube Hashing Large scale Computer center Principal component analysis
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:	Get full text
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