Sciweavers

Share
CVPR
2003
IEEE

Object Class Recognition by Unsupervised Scale-Invariant Learning

10 years 3 months ago
Object Class Recognition by Unsupervised Scale-Invariant Learning
We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion and relative scale. An entropy-based feature detector is used to select regions and their scale within the image. In learning the parameters of the scale-invariant object model are estimated. This is done using expectation-maximization in a maximum-likelihood setting. In recognition, this model is used in a Bayesian manner to classify images. The flexible nature of the model is demonstrated by excellent results over a range of datasets including geometrically constrained classes (e.g. faces, cars) and flexible objects (such as animals).
Robert Fergus, Pietro Perona, Andrew Zisserman
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2003
Where CVPR
Authors Robert Fergus, Pietro Perona, Andrew Zisserman
Comments (0)
books