Sciweavers

Share
CVPR
2008
IEEE

Conditional density learning via regression with application to deformable shape segmentation

11 years 6 months ago
Conditional density learning via regression with application to deformable shape segmentation
Many vision problems can be cast as optimizing the conditional probability density function p(C|I) where I is an image and C is a vector of model parameters describing the image. Ideally, the density function p(C|I) would be smooth and unimodal allowing local optimization techniques, such as gradient descent or simplex, to converge to an optimal solution quickly, while preserving significant nonlinearities of the model. We propose to learn a conditional probability density satisfying these desired properties for the given training data set. To do this, we formulate a novel regression problem that finds a function approximating the target density. Learning the regressor is challenging due to the high dimensionality of model parameters, C, and the complexity of relating the image and the model. Our approach makes two contributions. First, we take a multilevel refinement approach by learning a series of density functions, each of which guides the solution of optimization algorithms incre...
Jingdan Zhang, Shaohua Kevin Zhou, Dorin Comaniciu
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2008
Where CVPR
Authors Jingdan Zhang, Shaohua Kevin Zhou, Dorin Comaniciu, Leonard McMillan
Comments (0)
books