Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Action recognition in still images by latent superpixel classification   Action recognition from still images is an important task of computer vision
applications such as image annotation, robotic navigation, video surveillance
and several others. Existing approaches mainly rely on either bag-of-feature
representations or articulated body-part models. However, the relationship
between the action and the image segments is still substantially unexplored.
For this reason, in this paper we propose to approach action recognition by
leveraging an intermediate layer of "superpixels" whose latent classes can act
as attributes of the action. In the proposed approach, the action class is
predicted by a structural model(learnt by Latent Structural SVM) based on
measurements from the image superpixels and their latent classes. Experimental
results over the challenging Stanford 40 Actions dataset report a significant
average accuracy of 74.06% for the positive class and 88.50% for the negative
class, giving evidence to the performance of the proposed approach.

2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an