Non-Parametric Video Segmentation

Sudderth and Jordan has successfully proposed a non-parametric image segmentation engine by imposing a hierarchical pitman-yor prior on the data and training the probabilistic model through variation learning. We propose to extend the framework to the video domain by incorporating temporal and optical flow information. The project tackles the problems of scarcity of segmented, annotated video dataset, as well as computational issues via GPU computation and distributed computing (e.g., EC2, Hadoop clusters). For more information, contact Alex Shyr