Unsupervised video object segmentation using conditional random fields

In this work, we propose a graph-based superpixel segmentation technique to perform spatiotemporal oversegmentation of videos. The generated superpixels are post-processed by applying a straightforward threshold-based foreground separation model. These superpixels are used in a conditional random field, and a potential function is defined, which is solved using energy minimization techniques to produce a final segmentation. Experiments on two datasets containing over 24 videos demonstrate that our method produces competitive or better results for the video object segmentation task over the state-of-the-art algorithms.