Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Coarse2Fine: Two-Layer Fusion For Image Retrieval   This paper addresses the problem of large-scale image retrieval. We propose a
two-layer fusion method which takes advantage of global and local cues and
ranks database images from coarse to fine (C2F). Departing from the previous
methods fusing multiple image descriptors simultaneously, C2F is featured by a
layered procedure composed by filtering and refining. In particular, C2F
consists of three components. 1) Distractor filtering. With holistic
representations, noise images are filtered out from the database, so the number
of candidate images to be used for comparison with the query can be greatly
reduced. 2) Adaptive weighting. For a certain query, the similarity of
candidate images can be estimated by holistic similarity scores in
complementary to the local ones. 3) Candidate refining. Accurate retrieval is
conducted via local features, combining the pre-computed adaptive weights.
Experiments are presented on two benchmarks, \emph{i.e.,} Holidays and Ukbench
datasets. We show that our method outperforms recent fusion methods in terms of
storage consumption and computation complexity, and that the accuracy is
competitive to the state-of-the-arts.

16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin