Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic discovery of discriminative parts as a quadratic assignment
  problem   Part-based image classification consists in representing categories by small
sets of discriminative parts upon which a representation of the images is
built. This paper addresses the question of how to automatically learn such
parts from a set of labeled training images. The training of parts is cast as a
quadratic assignment problem in which optimal correspondences between image
regions and parts are automatically learned. The paper analyses different
assignment strategies and thoroughly evaluates them on two public datasets:
Willow actions and MIT 67 scenes. State-of-the art results are obtained on
these datasets.

30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc