Weakly Supervised Foreground Segmentation Based on Superpixel Grouping
Wangsheng Yu, Zhiqiang Hou, Peng Wang, Xianxiang Qin, Liguang Wang, Huanyu Li · IEEE Access · 2018
Image segmentation is of great significance to a variety of tasks in image processing and computer vision. Since fully unsupervised image segmentation is usually very hard in most cases, a task-oriented interactive segmentation approach becomes a popular solution. This paper proposes a weakly supervised image segmentation algorithm to extract foreground from a complex background relying only on a roughly predefined bounding-box. The algorithm integrates the Watershed algorithm and Mean-shift clustering algorithm to obtain reliable initial foreground and background labels for simple linear iterative clustering (SLIC) superpixels. Then, a synthetic superpixel grouping mechanism is proposed to group the remainder SLIC superpixels into foreground or background until the whole superpixels are completely grouped. The proposed algorithm reliefs the interactive information from users while maintaining the segmentation precision. Extensive experiments are performed, and the results indicate that the proposed algorithm can reliably segment the image foreground from the complex background with only a weakly supervision.