Texture segmentation is about dividing a texture-dominant image into multiple homogeneous texture regions. The existing unsupervised approaches for texture segmentation are annotation-free but often yield unsatisfactory results. In contrast, supervised approaches such as deep learning may have better performance but require a large amount of annotated data. In this letter, we propose a user-interactive approach to win the trade-off between unsupervised approaches and supervised deep approaches. Our approach requires the user to mark one pixel in each texture region, whose label is directly propagated to its neighbor region. Such labeled data are of very small amount and even partially erroneous. To effectively exploit such weakly-labeled data, we construct a weakly-supervised sparse coding model that jointly conducts feature learning and segmentation. In addition, the geometric constraints are developed for the model to exploit the geometric prior on the local connectivity of region boundaries. The experiments on two benchmark datasets have validated the effectiveness of the proposed approach.

中文翻译：

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用于交互式纹理分割的几何先验弱监督稀疏编码

纹理分割是将一个以纹理为主的图像划分为多个均匀的纹理区域。现有的无监督纹理分割方法是无注释的，但往往产生不令人满意的结果。相比之下，深度学习等监督方法可能有更好的性能，但需要大量带注释的数据。在这封信中，我们提出了一种用户交互方法来赢得无监督方法和有监督深度方法之间的权衡。我们的方法要求用户在每个纹理区域标记一个像素，其标签直接传播到其相邻区域。这种标记数据的数量非常少，甚至部分错误。为了有效地利用这种弱标记的数据，我们构建了一个弱监督的稀疏编码模型，该模型联合进行特征学习和分割。此外，为模型开发了几何约束，以利用区域边界局部连通性上的几何先验。在两个基准数据集上的实验验证了所提出方法的有效性。