用经验曲波变换提取纹理特征，提升图像分割精度
Empirical curvelet based Fully Convolutional Network for supervised texture image segmentation
- 用自适应经验曲波滤波器组提取纹理特征
- 在多个数据集上显著优于现有方法
- 适合需要高精度纹理分割的任务
本文提出一种新型监督式纹理分类/分割方法。核心思想是将特定纹理描述符输入全卷积网络。这些描述符通过经验曲波变换从图像中提取。我们提出一种构建自适应经验曲波滤波器组的方法，针对给定纹理字典进行优化。实验表明，该滤波器输出可生成高效纹理描述符，用于训练深度学习模型。方法在多个数据集上评估，结果显著优于多种前沿算法。
原文摘要 · Abstract (English)
In this paper, we propose a new approach to perform supervised texture classification/segmentation. The proposed idea is to feed a Fully Convolutional Network with specific texture descriptors. These texture features are extracted from images by using an empirical curvelet transform. We propose a method to build a unique empirical curvelet filter bank adapted to a given dictionary of textures. We then show that the output of these filters can be used to build efficient texture descriptors utilized to finally feed deep learning networks. Our approach is finally evaluated on several datasets and compare the results to various state-of-the-art algorithms and show that the proposed method dramatically outperform all existing ones.
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