Open AccessMulti‐bit quantisation for similarity‐preserving hashing
Author(s) -
Su Liang Liang,
Tang Jun,
Liang Dong,
Zhu Ming
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2017.0465
Subject(s) - hamming distance , hash function , projection (relational algebra) , pattern recognition (psychology) , similarity (geometry) , dimension (graph theory) , artificial intelligence , computer science , hamming space , nearest neighbor search , algorithm , mathematics , feature (linguistics) , image (mathematics) , hamming code , decoding methods , linguistics , philosophy , computer security , pure mathematics , block code
As a promising alternative to traditional search techniques, hashing‐based approximate nearest neighbour search provides an applicable solution for big data. Most existing efforts are devoted to finding better projections to preserve the neighbouring structure of original data points in Hamming space, but ignore the quantisation procedure which may lead to the breakdown of the neighbouring structure maintained in the projection stage. To address this issue, the authors propose a novel multi‐bit quantisation (MBQ) method using a Matthews correlation coefficient (MCC) term and a regularisation term. The authors' method utilises the neighbouring relationship and the distribution information of original data points instead of the projection dimension usually used in the previous MBQ methods to adaptively learn optimal quantisation thresholds, and allocates multiple bits per projection dimension in terms of the learned thresholds. Experiments on two typical image data sets demonstrate that the proposed method effectively preserves the similarity between data points in the original feature space and outperforms state‐of‐the‐art quantisation methods.