Unsupervised Video Segmentation via Spatio-Temporally Nonlocal Appearance Learning

Understand

Video object segmentation is challenging due to the factors like rapidly fast motion, cluttered backgrounds, arbitrary object appearance variation and shape deformation.

• Most existing methods only explore appearance information between two consecutive frames, which do not make full use of the usefully long-term nonlocal information that is helpful to make the learned appearance stable, and hence they tend to fail when the targets suffer from large viewpoint changes and significant non-rigid deformations.
• In this paper, we propose a simple yet effective approach to mine the long-term sptatio-temporally nonlocal appearance information for unsupervised video segmentation.
• The motivation of our algorithm comes from the spatio-temporal nonlocality of the region appearance reoccurrence in a video.