Unsupervised binary hashing method using locality preservation and quantisation error minimisation

Abstract

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.