Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Beyond $\chi^2$ Difference: Learning Optimal Metric for Boundary
  Detection   This letter focuses on solving the challenging problem of detecting natural
image boundaries. A boundary usually refers to the border between two regions
with different semantic meanings. Therefore, a measurement of dissimilarity
between image regions plays a pivotal role in boundary detection of natural
images. To improve the performance of boundary detection, a Learning-based
Boundary Metric (LBM) is proposed to replace $\chi^2$ difference adopted by the
classical algorithm mPb. Compared with $\chi^2$ difference, LBM is composed of
a single layer neural network and an RBF kernel, and is fine-tuned by
supervised learning rather than human-crafted. It is more effective in
describing the dissimilarity between natural image regions while tolerating
large variance of image data. After substituting $\chi^2$ difference with LBM,
the F-measure metric of mPb on the BSDS500 benchmark is increased from 0.69 to
0.71. Moreover, when image features are computed on a single scale, the
proposed LBM algorithm still achieves competitive results compared with
\emph{mPb}, which makes use of multi-scale image features.

29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org