  In the last decade, deep learning has contributed to advances in a wide range
computer vision tasks including texture analysis. This paper explores a new
approach for texture segmentation using deep convolutional neural networks,
sharing important ideas with classic filter bank based texture segmentation
methods. Several methods are developed to train Fully Convolutional Networks to
segment textures in various applications. We show in particular that these
networks can learn to recognize and segment a type of texture, e.g. wood and
grass from texture recognition datasets (no training segmentation). We
demonstrate that Fully Convolutional Networks can learn from repetitive
patterns to segment a particular texture from a single image or even a part of
an image. We take advantage of these findings to develop a method that is
evaluated on a series of supervised and unsupervised experiments and improve
the state of the art on the Prague texture segmentation datasets.
