Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Point Context: An Effective Shape Descriptor for RST-invariant
  Trajectory Recognition   Motion trajectory recognition is important for characterizing the moving
property of an object. The speed and accuracy of trajectory recognition rely on
a compact and discriminative feature representation, and the situations of
varying rotation, scaling and translation has to be specially considered. In
this paper we propose a novel feature extraction method for trajectories.
Firstly a trajectory is represented by a proposed point context, which is a
rotation-scale-translation (RST) invariant shape descriptor with a flexible
tradeoff between computational complexity and discrimination, yet we prove that
it is a complete shape descriptor. Secondly, the shape context is nonlinearly
mapped to a subspace by kernel nonparametric discriminant analysis (KNDA) to
get a compact feature representation, and thus a trajectory is projected to a
single point in a low-dimensional feature space. Experimental results show
that, the proposed trajectory feature shows encouraging improvement than
state-of-art methods.

31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio