Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Supervised Texture Classification Using a Novel Compression-Based
  Similarity Measure   Supervised pixel-based texture classification is usually performed in the
feature space. We propose to perform this task in (dis)similarity space by
introducing a new compression-based (dis)similarity measure. The proposed
measure utilizes two dimensional MPEG-1 encoder, which takes into consideration
the spatial locality and connectivity of pixels in the images. The proposed
formulation has been carefully designed based on MPEG encoder functionality. To
this end, by design, it solely uses P-frame coding to find the (dis)similarity
among patches/images. We show that the proposed measure works properly on both
small and large patch sizes. Experimental results show that the proposed
approach significantly improves the performance of supervised pixel-based
texture classification on Brodatz and outdoor images compared to other
compression-based dissimilarity measures as well as approaches performed in
feature space. It also improves the computation speed by about 40% compared to
its rivals.

2
The categories of this paper are: math
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex