Loading [a11y]/accessibility-menu.js
Linear Subspace Ranking Hashing for Cross-Modal Retrieval | IEEE Journals & Magazine | IEEE Xplore

Linear Subspace Ranking Hashing for Cross-Modal Retrieval


Abstract:

Hashing has attracted a great deal of research in recent years due to its effectiveness for the retrieval and indexing of large-scale high-dimensional multimedia data. In...Show More

Abstract:

Hashing has attracted a great deal of research in recent years due to its effectiveness for the retrieval and indexing of large-scale high-dimensional multimedia data. In this paper, we propose a novel ranking-based hashing framework that maps data from different modalities into a common Hamming space where the cross-modal similarity can be measured using Hamming distance. Unlike existing cross-modal hashing algorithms where the learned hash functions are binary space partitioning functions, such as the sign and threshold function, the proposed hashing scheme takes advantage of a new class of hash functions closely related to rank correlation measures which are known to be scale-invariant, numerically stable, and highly nonlinear. Specifically, we jointly learn two groups of linear subspaces, one for each modality, so that features' ranking orders in different linear subspaces maximally preserve the cross-modal similarities. We show that the ranking-based hash function has a natural probabilistic approximation which transforms the original highly discontinuous optimization problem into one that can be efficiently solved using simple gradient descent algorithms. The proposed hashing framework is also flexible in the sense that the optimization procedures are not tied upto any specific form of loss function, which is typical for existing cross-modal hashing methods, but ratherwe can flexibly accommodate different loss functions with minimal changes to the learning steps. We demonstrate through extensive experiments on four widely-used real-world multimodal datasets that the proposed cross-modal hashing method can achieve competitive performance against several state-of-the-arts with only moderate training and testing time.
Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence ( Volume: 39, Issue: 9, 01 September 2017)
Page(s): 1825 - 1838
Date of Publication: 19 September 2016

ISSN Information:

PubMed ID: 27662669

Funding Agency:


1 Introduction

Thanks to the rapid advancement of information technologies, the last decade has witnessed unprecedented growth in multimedia content generated by all kinds of digital electronic devices, such as digital cameras, mobile phones and tablets etc. With its massive and quickly-increasing volume, multimedia data calls for efficient techniques to support effective indexing and fast similarity search based on semantic content.

Contact IEEE to Subscribe

References

References is not available for this document.