New features for affine-invariant shape classification

An object seen from different viewpoints results in differently deformed images. Affine-invariant shape classification must classify correctly the object, regardless its viewpoint. In this paper, we propose new local and global features invariant under affine transformation. These features can be used for supervised or unsupervised shape classification, and for shape-based image indexing and retrieval. One of the proposed features is related to the convex deficiency and the others are extracted from the area matrix. Area matrix was used by Shen for the similarity matching in image retrieval. However, differently from the Shen´s work, we parameterize the shape contour using the affine-length parameter. This makes our features robust to affine parameterization, while Shen´s work does not have this property. Experimental results indicate that our method can classify correctly even highly deformed and noisy shapes using small training sets.