Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Texture segmentation with Fully Convolutional Networks   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.

16
The categories of this paper are: stat
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th