Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Incremental Learning of 3D-DCT Compact Representations for Robust Visual
  Tracking   Visual tracking usually requires an object appearance model that is robust to
changing illumination, pose and other factors encountered in video. In this
paper, we construct an appearance model using the 3D discrete cosine transform
(3D-DCT). The 3D-DCT is based on a set of cosine basis functions, which are
determined by the dimensions of the 3D signal and thus independent of the input
video data. In addition, the 3D-DCT can generate a compact energy spectrum
whose high-frequency coefficients are sparse if the appearance samples are
similar. By discarding these high-frequency coefficients, we simultaneously
obtain a compact 3D-DCT based object representation and a signal
reconstruction-based similarity measure (reflecting the information loss from
signal reconstruction). To efficiently update the object representation, we
propose an incremental 3D-DCT algorithm, which decomposes the 3D-DCT into
successive operations of the 2D discrete cosine transform (2D-DCT) and 1D
discrete cosine transform (1D-DCT) on the input video data.

3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph