Although the algorithm was originally designed for videos, virtually all implementations use SIOX primarily for still image segmentation.
Technically, the algorithm performs the following steps: For video segmentation the sure background and sure foreground regions are learned from motion statistics.
SIOX performs about equally well on different benchmarks compared to graph-based segmentation methods, such as Grabcut.
Graph-based segmentation methods search for a minimum cut and therefore tend to not perform optimally with complex structures.
The main developer, Gerald Friedland, is now faculty at the EECS department of the University of California at Berkeley and also a Principal Data Scientist at Lawrence Livermore National Lab.