"Learning the kernel" through examples: an application to shape classification

One important problem in any retrieval system is the design of good features and of good similarity measures between features. Usually these similarity functions are defined through ad-hoc distances between features. We propose a new way to design such distances, based on non-rigid deformation of nonlinear principal components, in the framework of semi-parametric statistical regression. The proposed approach is applied to the construction of new rotation invariant distance between planar curves