Hash Bit Selection Using Markov Process for Approximate Nearest Neighbor Search

Authors:
Danchen Zhang;Xianglong Liu;Bo Lang
Affiliations:
State Key Lab of Software Development Environment, Beihang University, Beijing, China;State Key Lab of Software Development Environment, Beihang University, Beijing, China;State Key Lab of Software Development Environment, Beihang University, Beijing, China
Venue:
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
Year:
2013

Citing 6
Cited 0

Dominant Sets and Pairwise Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Compact hashing with joint optimization of search accuracy and time

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Random maximum margin hashing

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Spherical hashing

CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Mobile product search with Bag of Hash Bits and boundary reranking

CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Hash Bit Selection: A Unified Solution for Selection Problems in Hashing

CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Hashing for nearest neighbor search has attracted great attentions in the past years. Many hashing methods have been successfully applied in real-world applications like the mobile product search. The performance of these applications usually highly relies on the quality of hash bits. However, it still lacks of a general method that can provide good hash bits for different scenarios. In this paper, we propose a novel method that can select compact, independent and informative hash bits using the Markov Process. Our method can serve as a unified framework compatible with different hashing methods. We design two algorithms, BS-CMP and BS-DMP, and formulate the selection problem as the subgraph discovery on a graph. Experiments are conducted for two important selection scenarios when applying hash techniques, i.e., hashing using different hashing algorithms and hashing with multiple features. The result indicates that our proposed bit selection approaches outperform naive selection methods significantly under aforementioned two scenarios.