Texture segmentation with Fully Convolutional Networks
This addresses texture segmentation in computer vision, offering incremental improvements for applications like image analysis.
The paper tackles texture segmentation by training Fully Convolutional Networks to recognize and segment textures like wood and grass from datasets without segmentation training, improving state-of-the-art results on the Prague texture segmentation datasets.
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.