PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Asymmetric Distances for Binary Embeddings
Albert Gordo and Florent Perronnin
In: CVPR 2011, 20-25 June 2011, Colorado Springs, USA.

Abstract

In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), SpectralHashing (SH) and Semi-Supervised Hashing (SSH). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. We also propose a novel simple binary embedding technique – PCA Embedding (PCAE) – which is shown to yield competitive results with respect to more complex algorithms such as SH and SSH.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:8625
Deposited By:Florent Perronnin
Deposited On:16 February 2012