Rotation invariant deep binary hashing for fast image retrieval

Lai Dai; Jianming Liu; Aiwen Jiang

doi:10.1117/12.2281692

21 July 2017 Rotation invariant deep binary hashing for fast image retrieval

Lai Dai, Jianming Liu, Aiwen Jiang

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104201Z (2017) https://doi.org/10.1117/12.2281692
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

Abstract

In this paper, we study how to compactly represent image’s characteristics for fast image retrieval. We propose supervised rotation invariant compact discriminative binary descriptors through combining convolutional neural network with hashing. In the proposed network, binary codes are learned by employing a hidden layer for representing latent concepts that dominate on class labels. A loss function is proposed to minimize the difference between binary descriptors that describe reference image and the rotated one. Compared with some other supervised methods, the proposed network doesn’t have to require pair-wised inputs for binary code learning. Experimental results show that our method is effective and achieves state-of-the-art results on the CIFAR-10 and MNIST datasets.

Citation Download Citation

Lai Dai, Jianming Liu, and Aiwen Jiang "Rotation invariant deep binary hashing for fast image retrieval", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104201Z (21 July 2017); https://doi.org/10.1117/12.2281692

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