Part-based Probabilistic Point Matching using Equivalence Constraints
Graham McNeill and Sethu Vijayakumar
In: Advances in Neural Information Processing Systems 19, Dec 2006, Vancouver, Canada.
Correspondence algorithms typically struggle with shapes that display part-based
variation. We present a probabilistic approach that matches shapes using independent
part transformations, where the parts themselves are learnt during matching.
Ideas from semi-supervised learning are used to bias the algorithm towards finding
‘perceptually valid’ part structures. Shapes are represented by unlabeled point
sets of arbitrary size and a background component is used to handle occlusion,
local dissimilarity and clutter. Thus, unlike many shape matching techniques, our
approach can be applied to shapes extracted from real images. Model parameters
are estimated using an EM algorithm that alternates between finding a soft
correspondence and computing the optimal part transformations using Procrustes