Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A survey on deep hashing for image retrieval   Hashing has been widely used in approximate nearest search for large-scale
database retrieval for its computation and storage efficiency. Deep hashing,
which devises convolutional neural network architecture to exploit and extract
the semantic information or feature of images, has received increasing
attention recently. In this survey, several deep supervised hashing methods for
image retrieval are evaluated and I conclude three main different directions
for deep supervised hashing methods. Several comments are made at the end.
Moreover, to break through the bottleneck of the existing hashing methods, I
propose a Shadow Recurrent Hashing(SRH) method as a try. Specifically, I devise
a CNN architecture to extract the semantic features of images and design a loss
function to encourage similar images projected close. To this end, I propose a
concept: shadow of the CNN output. During optimization process, the CNN output
and its shadow are guiding each other so as to achieve the optimal solution as
much as possible. Several experiments on dataset CIFAR-10 show the satisfying
performance of SRH.

32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom