On Affine Invariant Clustering and Automatic Cast Listing in Movies

Abstract We develop a distance metric for clustering and classification algorithms which is invariant to affine transformations and includes priors on the transformation parameters. Such clustering requirements are generic to a number of problems in computer vision. We extend existing techniques for affine-invariant clustering, and show that the new distance metric outperforms existing approximations to affine invariant distance computation, particularly under large transformations. In addition, we incorporate prior probabilities on the transformation parameters. This further regularizes the solution, mitigating a rare but serious tendency of the existing solutions to diverge. For the particular special case of corresponding point sets we demonstrate that the affine invariant measure we introduced may be obtained in closed form. As an application of these ideas we demonstrate that the faces of the principal cast of a feature film can be generated automatically using clustering with app...

• Type: Conference
• Year: 2002
• Where: ECCV
• Authors: Andrew W. Fitzgibbon, Andrew Zisserman
• Keywords: Affine Invariant Measure, Affine-invariant Clustering, Computer Vision, Distance Metric Outperforms, ECCV 2002, Invariant Distance Computation, Such Clustering Requirements