Feature Learning based Deep Supervised Hashing with Pairwise Labels | Awesome Learning to Hash Add your paper to Learning2Hash

Awesome Learning to Hash

A webpage dedicated to the latest research on learning-to-hash, including state-of-the-art deep hashing models, all updated on a weekly basis. Maintained by Sean Moran.

Contact Sean Moran about this survey or website. Made with Jekyll and Hyde.

Feature Learning based Deep Supervised Hashing with Pairwise Labels

Wu-Jun Li, Sheng Wang and Wang-Cheng Kang. IJCAI 2016

[PDF] [Code]    
IJCAI Deep Learning Has Code Image Retrieval

Recent years have witnessed wide application of hashing for large-scale image retrieval. However, most existing hashing methods are based on handcrafted features which might not be optimally compatible with the hashing procedure. Recently, deep hashing methods have been proposed to perform simultaneous feature learning and hash-code learning with deep neural networks, which have shown better performance than traditional hashing methods with hand-crafted features. Most of these deep hashing methods are supervised whose supervised information is given with triplet labels. For another common application scenario with pairwise labels, there have not existed methods for simultaneous feature learning and hash-code learning. In this paper, we propose a novel deep hashing method, called deep pairwise-supervised hashing (DPSH), to perform simultaneous feature learning and hashcode learning for applications with pairwise labels. Experiments on real datasets show that our DPSH method can outperform other methods to achieve the state-of-the-art performance in image retrieval applications.

Similar Work