Semi-supervised shape classification based on low rank constraint active contour

Abstract

In this paper, a novel approach is proposed for shape classification based on the semi-supervised framework. For shape similarity-measuring problem, in order to avoid solving an NP problem produced by finding some affine transformation and to enhance its robustness for local changes of the shapes, we switch to compute an energy index defined by the degree of segmentation. The corresponding segmentation is achieved by active contour modeling with the low-rank constraint by the prior shape. Then, the sequence of shapes is classified into a certain number of categories by repeating this scheme in a semisupervised framework. The experiment results showed the feasibility of our model.