PT Journal
AU Albert Gordo
   Florent Perronnin
   Yunchao Gong
   Svetlana Lazebnik
TI Asymmetric Distances for Binary Embeddings
SO IEEE Transactions on Pattern Analysis and Machine Intelligence
JI TPAMI
PY 2014
BP 33
EP 47
VL 36
IS 1
DI 10.1109/TPAMI.2013.101
AB 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), Spectral Hashing (SH), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). 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.
ER