With
the advent of the era of big data, the storage and retrieval of data have
become a research hotspot. Hashing methods that transform high-dimensional data
into compact binary codes have received increasing attention. Recently, with
the successful application of convolutional neural networks in computer vision,
deep hashing methods utilize an end-to-end framework to learn feature
representations and hash codes mutually, which achieve better retrieval performance
than conventional hashing methods. However, deep hashing methods still face
some challenges in image retrieval. Firstly, most existing deep hashing methods
preserve similarity between original data space and hash coding space using
loss functions with high time complexity, which cannot get a win-win situation
in time and accuracy. Secondly, few existing deep hashing methods are designed
for fine-grained image retrieval, which is necessary in practice. In this
study, we propose a novel semantics-preserving hashing method which solves the
above problems. We add a hash layer before the classification layer as a
feature switch layer to guide the classification. At the same time, we replace
the complicated loss with the simple classification loss, combining with
quantization loss and bit balance loss to generate high-quality hash codes.
Besides, we incorporate feature extractor designed for fine-grained image
classification into our network for better representation learning. The results
on three widely-used fine-grained image datasets show that our method is
superior to other state-of-the-art image retrieval methods.
Funding
This work was supported by the Fundamental Research Funds for the Central Universities under Grant NS2016091
History
Citation
IEEE Access, 2020, Vol 8
Author affiliation
Department of Informatics
Version
VoR (Version of Record)
Published in
IEEE Access
Volume
8
Publisher
Institute of Electrical and Electronics Engineers (IEEE)