Robust deep supervised hashing for image retrieval

Zhaoguo Mo; Yuesheng Zhu; Jiawei Zhan

doi:10.1117/12.2573083

12 June 2020 Robust deep supervised hashing for image retrieval

Zhaoguo Mo, Yuesheng Zhu, Jiawei Zhan

Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 115191T (2020) https://doi.org/10.1117/12.2573083
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

Abstract

Hashing is an important technique branch of image retrieval due to its satisfactory retrieval performance with high retrieval speed and low storage cost. Deep supervised hashing methods, which take advantage of the convolutional neural network and the supervised information, have shown better performance than other kinds of hashing methods. However, previous deep hashing methods do not consider the noisy data, which generally exist in large-scale labeled datasets and mislead the learning algorithm. In this paper, we propose a novel robust deep supervised hashing (RDSH) method, in which a robust pairwise loss and a quantitation loss are used to supervise the learning procedure. The quantitation loss guides the CNN to output binary codes. The robust pairwise loss for similarity-preserving learning is designed based on generalizations of the exponential and logarithmic functions. By adjusting its parameters, the robust pairwise loss can exhibit special properties, including tail-heaviness and boundedness, which results in the robustness of the learning procedure to noisy training data. To verify the robustness of the RDSH, we conduct experiments on CIFAR10 with different noisy levels, in which RDSH shows better robustness than other deep supervised hashing methods. Experiments on standard CIFAR-10 and NUS-WIDE datasets show that RDSH outperforms other baselines.

Citation Download Citation

Zhaoguo Mo, Yuesheng Zhu, and Jiawei Zhan "Robust deep supervised hashing for image retrieval", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 115191T (12 June 2020); https://doi.org/10.1117/12.2573083

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
13 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image retrieval

Binary data

Quantization

Convolutional neural networks

Feature extraction

Data modeling

Image compression

Keywords/Phrases

Search In:

Publication Years