Rui Hu, Diane Larlus, Gabriela Csurka
The 8th Indian Conference on Vision, Graphics and Image Processing, Bombay, India, December 16-19, 2012.
There is a general trend in recent methods to use image regions
(i.e. super-pixels) obtained in an unsupervised way to
enhance the semantic image segmentation task. This paper
proposes a detailed study on the role and the benefit of
using these regions, at different steps of the segmentation
process. For the purpose of this benchmark, we propose a
simple system for semantic segmentation that uses a hierarchy
of regions. A patch based system with similar settings
is compared, which allows us to evaluate the contribution of
each component of the system. Both systems are evaluated
on the standard MSRC-21 dataset and obtain competitive
results. We show that the proposed region based system
can achieve good results without any complex regularization,
while its patch based counterpart becomes competitive
when using image prior and regularization methods. The
latter benefit more from a CRF based regularization, yielding
to state-of-the-art results with simple constraints based
only on the leaf regions exploited in the pairwise potential.
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