Abstract
We address the problem of texture segmentation by
grouping dense pixel-wise descriptors. We introduce and
construct learned Shape-Tailored Descriptors that aggregate image statistics only within regions of interest to avoid
mixing statistics of different textures, and that are invariant
to complex nuisances (e.g., illumination, perspective and
deformations). This is accomplished by training a neural
network to discriminate base shape-tailored descriptors of
oriented gradients at various scales. These descriptors are
defined through partial differential equations to obtain data
at various scales in arbitrarily shaped regions. We formulate and optimize a joint optimization problem in the segmentation and descriptors to discriminate these base descriptors using the learned metric, equivalent to grouping
learned descriptors. Experiments on benchmark datasets
show that the descriptors learned on a small dataset of segmented images generalize well to unseen textures in other
datasets, showing the generic nature of these descriptors.
We also show state-of-the-art results on texture segmentation benchmarks