Sparse multi-modal hashing

Wu, Fei, Yu, Zhou, Yang, Yi, Tang, Siliang, Zhang, Yin and Zhuang, Yueting (2014) Sparse multi-modal hashing. IEEE Transactions on Multimedia, 16 2: 427-439. doi:10.1109/TMM.2013.2291214


Author Wu, Fei
Yu, Zhou
Yang, Yi
Tang, Siliang
Zhang, Yin
Zhuang, Yueting
Title Sparse multi-modal hashing
Journal name IEEE Transactions on Multimedia   Check publisher's open access policy
ISSN 1520-9210
1941-0077
Publication date 2014-01-01
Year available 2014
Sub-type Article (original research)
DOI 10.1109/TMM.2013.2291214
Open Access Status Not yet assessed
Volume 16
Issue 2
Start page 427
End page 439
Total pages 13
Place of publication Piscataway, NJ United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Subject 2208 Electrical and Electronic Engineering
1711 Signal Processing
2214 Media Technology
1706 Computer Science Applications
Abstract Learning hash functions across heterogenous high-dimensional features is very desirable for many applications involving multi-modal data objects. In this paper, we propose an approach to obtain the sparse codesets for the data objects across different modalities via joint multi-modal dictionary learning, which we call sparse multi-modal hashing (abbreviated as SM2. In SM2, both intra-modality similarity and inter-modality similarity are first modeled by a hypergraph, then multi-modal dictionaries are jointly learned by Hypergraph Laplacian sparse coding. Based on the learned dictionaries, the sparse codeset of each data object is acquired and conducted for multi-modal approximate nearest neighbor retrieval using a sensitive Jaccard metric. The experimental results show that SM2 outperforms other methods in terms of mAP and Percentage on two real-world data sets.
Keyword Dictionary learning
Multi modal hashing
Sparse coding
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
 
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