Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: NIST: An Image Classification Network to Image Semantic Retrieval   This paper proposes a classification network to image semantic retrieval
(NIST) framework to counter the image retrieval challenge. Our approach
leverages the successful classification network GoogleNet based on
Convolutional Neural Networks to obtain the semantic feature matrix which
contains the serial number of classes and corresponding probabilities. Compared
with traditional image retrieval using feature matching to compute the
similarity between two images, NIST leverages the semantic information to
construct semantic feature matrix and uses the semantic distance algorithm to
compute the similarity. Besides, the fusion strategy can significantly reduce
storage and time consumption due to less classes participating in the last
semantic distance computation. Experiments demonstrate that our NIST framework
produces state-of-the-art results in retrieval experiments on MIRFLICKR-25K
dataset.

10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
19
The categories of this paper are: eess