Weakly-Supervised Sparse Coding With Geometric Prior for Interactive Texture Segmentation

Yuhui Quan, Huan Teng, Tao Liu, Yan Huang

Abstract: 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.