Affine-invariant contours recognition using an incremental hybrid learning approach

In this paper, a planar shape recognition system is proposed. This proposal is based on a global incremental scheme which combines two learning mechanisms: the Incremental Non-parametric Discriminant Analysis and the mode analysis method. At the feature selection stage, a novel adaptive curvature estimator for shape characterization is presented. This method describes the planar shape using an affine-invariant triangle-area representation obtained from its closed contour. Contrary to previous approaches, the triangle side lengths at each contour point are adapted to the local variations of the shape, removing noise from the contour without missing relevant points. In order to reduce the dimensionality of the shape descriptor, an Incremental Non-parametric Discriminant Analysis is conducted to seek directions for efficient discrimination (incremental eigenspace learning). At the classification stage, the incremental mode analysis is employed to classify feature vectors into a set of spherically-shaped groups (incremental prototype learning). The classification is conducted based on the k-nearest neighbor approach whose prototypes are updated by the mode analysis method. This scheme enables a classifier to learn incrementally, on-line, and in one-pass. Experimental results show that the proposed shape recognition system is well suited for shape indexing and retrieval.