Reverse Image Segmentation: A High-Level Solution to a Low-Level Task
Jiajun Wu, Jun-Yan Zhu, Zhuowen Tu · 2014
Image segmentation is known to be an ambiguous problem whose solution needs an integration of image and shape cues of various levels; using low-level information alone is often not sufficient for a segmentation algorithm to match human capability. Two recent trends are popular in this area: (1) low-level and mid-level cues are combined to-gether in learning-based approaches to localize segmentation boundaries; (2) high-level vision tasks such as image labeling and object recognition are directly performed to ob-tain object boundaries. In this paper, we present an interesting observation that performs image segmentation in a reverse way, i.e., using a high-level semantic labeling approach to address a low-level segmentation problem, could be a proper solution. We perform semantic labeling on input images and derive segmentations from the labeling results. We adopt graph coloring theory to connect these two tasks and provide theoretical in-sights to our solution. This seemingly unusual way of doing image segmentation leads to surprisingly encouraging results, superior or comparable to those of the state-of-the-art image segmentation algorithms on multiple publicly available datasets.