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

Author

Trouvé, Alain ; Yu, Yong

Author_Institution

Univ. Paris 13, Villetaneuse, France

Volume

2

fYear

2001

Firstpage

121

Abstract

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

Keywords

feature extraction; image classification; image retrieval; image sequences; learning by example; principal component analysis; PCA; ad-hoc distances design; feature similarity measures; learning the kernel through examples; nonlinear principal components; nonrigid deformation; planar curves; principal component analysis; random sequence; retrieval system; rotation invariant distance; semi-parametric statistical regression; similarity functions; Bayesian methods; Embedded computing; Hilbert space; Kernel; Polynomials; Principal component analysis; Shape measurement; Statistical learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2001. Proceedings. 2001 International Conference on

Conference_Location

Thessaloniki

Print_ISBN

0-7803-6725-1

Type

conf

DOI

10.1109/ICIP.2001.958439

Filename

958439