Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35508
Title: A sparse embedding and least variance encoding approach to hashing
Authors: Zhu, X
Zhang, L 
Huang, Z
Keywords: Dictionary learning
Hashing
Image retrieval
Manifold learning
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2014, v. 23, no. 9, 6844160, p. 3737-3750 How to cite?
Journal: IEEE transactions on image processing 
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.
URI: http://hdl.handle.net/10397/35508
ISSN: 1057-7149 (print)
1941-0042 (online)
DOI: 10.1109/TIP.2014.2332764
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

44
Last Week
8
Last month
Citations as of Jul 20, 2017

WEB OF SCIENCETM
Citations

41
Last Week
0
Last month
Citations as of Jul 18, 2017

Page view(s)

25
Last Week
2
Last month
Checked on Jul 16, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.