  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.
