A sparse embedding and least variance encoding approach to hashing

Zhu, Xiaofeng, Zhang, Lei and Huang, Zi (2014) A sparse embedding and least variance encoding approach to hashing. IEEE Transactions on Image Processing, 23 9: 3737-3750. doi:10.1109/TIP.2014.2332764

Author Zhu, Xiaofeng
Zhang, Lei
Huang, Zi
Title A sparse embedding and least variance encoding approach to hashing
Journal name IEEE Transactions on Image Processing   Check publisher's open access policy
ISSN 1057-7149
Publication date 2014-09
Year available 2014
Sub-type Article (original research)
DOI 10.1109/TIP.2014.2332764
Open Access Status
Volume 23
Issue 9
Start page 3737
End page 3750
Total pages 14
Place of publication Piscataway NJ United States
Publisher Institute of Electrical and Electronics Engineers
Collection year 2015
Language eng
Abstract Hashing is becoming increasingly important in large-scale image retrieval for fast approximate similarity search and efficient data storage. Many popular hashing methods aim to preserve the kNN graph of high dimensional data points in the low dimensional manifold space, which is, however, difficult to achieve when the number of samples is big. In this paper, we propose an effective and efficient hashing approach by sparsely embedding a sample in the training sample space and encoding the sparse embedding vector over a learned dictionary. To this end, we partition the sample space into clusters via a linear spectral clustering method, and then represent each sample as a sparse vector of normalized probabilities that it falls into its several closest clusters. This actually embeds each sample sparsely in the sample space. The sparse embedding vector is employed as the feature of each sample for hashing. We then propose a least variance encoding model, which learns a dictionary to encode the sparse embedding feature, and consequently binarize the coding coefficients as the hash codes. The dictionary and the binarization threshold are jointly optimized in our model. Experimental results on benchmark data sets demonstrated the effectiveness of the proposed approach in comparison with state-of-the-art methods.
Keyword Hashing
Dictionary learning
Image retrieval
Manifold learning
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
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 18 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 16 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 12 Aug 2014, 03:31:19 EST by System User on behalf of School of Information Technol and Elec Engineering