Jump to : Download | Abstract | Contact | BibTex reference | EndNote reference |


Jun Wang, Sanjiv Kumar, Shih-Fu Chang. Semi-Supervised Hashing for Scalable Image Retrieval. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, USA, June 2010.

Download [help]

Download paper: Adobe portable document (pdf)

Copyright notice:This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.


Large scale image search has recently attracted considerable attention due to easy availability of huge amounts of data. Several hashing methods have been proposed to allow approximate but highly efficient search. Unsupervised hashing methods show good performance with metric distances but, in image search, semantic similarity is usually given in terms of labeled pairs of images. There exist supervised hashing methods that can handle such semantic similarity but they are prone to overfitting when labeled data is small or noisy. Moreover, these methods are usually very slow to train. In this work, we propose a semi-supervised hashing method that is formulated as minimizing empirical error on the labeled data while maximizing variance and independence of hash bits over the labeled and unlabeled data. The proposed method can handle both metric as well as semantic similarity. The experimental results on two large datasets (up to one million samples) demonstrate its superior performance over state-of-the-art supervised and unsupervised methods


Jun Wang
Shih-Fu Chang

BibTex Reference

   Author = {Wang, Jun and Kumar, Sanjiv and Chang, Shih-Fu},
   Title = {Semi-Supervised Hashing for Scalable Image Retrieval},
   BookTitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)},
   Address = {San Francisco, USA},
   Month = {June},
   Year = {2010}

EndNote Reference [help]

Get EndNote Reference (.ref)


For problems or questions regarding this web site contact The Web Master.

This document was translated automatically from BibTEX by bib2html (Copyright 2003 © Eric Marchand, INRIA, Vista Project).