Discrete Hashing With Multiple Supervision

Supervised hashing methods have achieved more promising results than unsupervised ones by leveraging label information to generate compact and accurate hash codes. Most of the prior supervised hashing methods construct an n × n instance-pairwise similarity matrix, where n is the number of training s...

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Bibliographic Details
Published in:IEEE transactions on image processing 2019-06, Vol.28 (6), p.2962-2975
Main Authors: Luo, Xin, Zhang, Peng-Fei, Huang, Zi, Nie, Liqiang, Xu, Xin-Shun
Format: Article
Language:English
Subjects:
Algorithms
Approximate nearest neighbor search
Binary codes
Codes
discrete hashing
Hash functions
image semantic indexing
Iterative methods
learning to hash
Matrix decomposition
Optimization
Quantization (signal)
Semantics
Similarity
similarity search
supervised hashing
Supervision
Training
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Summary:Supervised hashing methods have achieved more promising results than unsupervised ones by leveraging label information to generate compact and accurate hash codes. Most of the prior supervised hashing methods construct an n × n instance-pairwise similarity matrix, where n is the number of training samples. Nevertheless, this kind of similarity matrix results in high memory space cost and makes the optimization time-consuming, making it unacceptable in many real applications. In addition, most of the methods relax the discrete constraints to solve the optimization problem, which may cause large quantization errors and finally lead to poor performance. To address these limitations, in this paper, we present a novel hashing method, named discrete hashing with multiple supervision (MSDH). MSDH supervises the hash code learning with both class-wise and instance-class similarity matrices, whose space cost is much less than the instance-pairwise similarity matrix. With multiple supervision information, better hash codes can be learned. Besides, an iterative optimization algorithm is proposed to directly learn the discrete hash codes instead of relaxing the binary constraints. Experimental results on several widely used benchmark datasets demonstrate that MSDH outperforms some state-of-the-art methods.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2019.2892703