Supervised discrete discriminant hashing for image retrieval

Highlights：

• We develop a new supervised discrete discriminant hashing learning method, which can learn discrete hashing codes and hashing function simultaneously.

• To make the learned discrete hash codes to be optimal for classification, the learned hashing framework aims to learn a robust similarity metric so as to maximize the similarity of the same class discrete hash codes and minimize the similarity of the different class discrete hash codes simultaneously.

• To make the learned hash function for achieving optimal approximate discrete hash codes, the hash functions are optimized based on the directly learned discrete hash codes.

摘要

•We develop a new supervised discrete discriminant hashing learning method, which can learn discrete hashing codes and hashing function simultaneously.•To make the learned discrete hash codes to be optimal for classification, the learned hashing framework aims to learn a robust similarity metric so as to maximize the similarity of the same class discrete hash codes and minimize the similarity of the different class discrete hash codes simultaneously.•To make the learned hash function for achieving optimal approximate discrete hash codes, the hash functions are optimized based on the directly learned discrete hash codes.

论文关键词：Supervised hash learning,Discrete hash learning,Discrete hash codes,Discriminant information,Robust similarity metric

论文评审过程：Received 13 August 2017, Revised 18 November 2017, Accepted 7 January 2018, Available online 11 January 2018, Version of Record 2 February 2018.