DocumentCode :
70447
Title :
A Sparse Embedding and Least Variance Encoding Approach to Hashing
Author :
Xiaofeng Zhu ; Lei Zhang ; Zi Huang
Author_Institution :
Coll. of Comput. Sci. & Inf. Technol., Guangxi Normal Univ., Guilin, China
Volume :
23
Issue :
9
fYear :
2014
fDate :
Sept. 2014
Firstpage :
3737
Lastpage :
3750
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.
Keywords :
cryptography; encoding; file organisation; graph theory; probability; approximate similarity search; binarization threshold; data storage; hash codes; hashing; high dimensional data points; kNN graph; large-scale image retrieval; learned dictionary; least variance encoding; linear spectral clustering; low dimensional manifold space; normalized probability; sparse embedding feature; sparse embedding vector; sparse vector; training sample space; Binary codes; Dictionaries; Image coding; Time complexity; Training; Transforms; Vectors; Hashing; dictionary learning; image retrieval; manifold learning;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2014.2332764
Filename :
6844160
Link To Document :
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