Open AccessUnsupervised binary hashing method using locality preservation and quantisation error minimisation
Author(s) -
Kim JinBum,
Park RaeHong
Publication year - 2015
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2014.3980
Subject(s) - minimisation (clinical trials) , binary number , locality sensitive hashing , locality , hash function , algorithm , computer science , mathematics , artificial intelligence , pattern recognition (psychology) , hash table , statistics , arithmetic , linguistics , philosophy , computer security
An unsupervised binary hashing (UBH) method is proposed. To preserve the local and Euclidean metric structures in the reduced feature space, it performs the dimensionality reduction (DR) by using the orthogonal locality‐preserving projection. In addition, it minimises the error between the generated binary hash codes and low‐dimensional feature vectors that are obtained in DR. To minimise the quantisation error, the binary hash codes are generated using the optimal rotation and offset. Experimental results show that the proposed UBH method has better performance than other existing methods in terms of the mean average precision and recall–precision curve.