Bayesian Clustering of Optical Flow Fields

We present a method for unsupervised learning of classes of motions in video. We project optical flow fields to a complete, orthogonal, a-priori set of basis functions in a probabilistic fashion, which improves the estimation of the projections by incorporating uncertainties in the flows. We then cluster the projections using a mixture of feature-weighted Gaussians over optical flow fields. The resulting model extracts a concise probabilistic description of the major classes of optical flow present. The method is demonstrated on a video of a person's facial expressions.

• Type: Conference
• Year: 2003
• Where: ICCV
• Authors: Jesse Hoey, James J. Little
• Keywords: Computer Vision, Concise Probabilistic Description, ICCV 2003, Optical Flow Fields, Optical Flow Present, Probabilistic Fashion, Unsupervised Learning