An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Handbook of Face Recognition
Bayesian tangent shape model: Estimating shape and pose parameters via bayesian inference
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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This work presents a new approach to analysis of shapes represented by finite set of landmarks, that generalizes the notion of Procrustes distance - an invariant metric under translation, scaling, and rotation. In many shape classification tasks there is a large variability in certain landmarks due to intra-class and/or inter-class variations. Such variations cause poor shape alignment needed for Procrustes distance computation, and lead to poor classification performance. We apply a general framework to the task of supervised classification of shapes that naturally deals with landmark distributions exhibiting large intra class or inter-class variabilty. The incorporation of Procrustes metric and of a learnt general quadratic distance inspired by Fisher linear discriminant objective function, produces a generalized Procrustes distance. The learnt distance retains the invariance properties and emphasizes the discriminative shape features. In addition, we show how the learnt metric can be useful for kernel machines design and demonstrate a performance enhancement accomplished by the learnt distances on a variety of classification tasks of organismal forms datasets.