1 research outputs found
Probabilistic Semantic Segmentation Refinement by Monte Carlo Region Growing
Semantic segmentation with fine-grained pixel-level accuracy is a fundamental
component of a variety of computer vision applications. However, despite the
large improvements provided by recent advances in the architectures of
convolutional neural networks, segmentations provided by modern
state-of-the-art methods still show limited boundary adherence. We introduce a
fully unsupervised post-processing algorithm that exploits Monte Carlo sampling
and pixel similarities to propagate high-confidence pixel labels into regions
of low-confidence classification. Our algorithm, which we call probabilistic
Region Growing Refinement (pRGR), is based on a rigorous mathematical
foundation in which clusters are modelled as multivariate normally distributed
sets of pixels. Exploiting concepts of Bayesian estimation and variance
reduction techniques, pRGR performs multiple refinement iterations at varied
receptive fields sizes, while updating cluster statistics to adapt to local
image features. Experiments using multiple modern semantic segmentation
networks and benchmark datasets demonstrate the effectiveness of our approach
for the refinement of segmentation predictions at different levels of
coarseness, as well as the suitability of the variance estimates obtained in
the Monte Carlo iterations as uncertainty measures that are highly correlated
with segmentation accuracy.Comment: Submitted to IEEE Transactions on Image Processing (April 2020