Eigen and Fisher Barycenter Contour for 2D Shape Classification

Abstract

To achieve a good performance for shape classification, it requires both shape representation and classifier. In this paper, the so-called Eigen Barycenter Contour (EBcC) and Fisher Barycenter Contour (FBcC) techniques are presented for 2D shape classification. The representation utilizes the area of triangles at different scale level of Barycenter Contour (BcC). However, it is not invariant to starting point selection, so the phase normalization is applied. After that, we linearly project the shape feature in 3D format onto a subspace based on EBcC technique into low dimensional subspace. The FBcC, another similar method, also produces well separated classes in low dimensional subspace. Finally, the normalized cross correlation is used to measure the similarity among shapes. The experimental results demonstrate that the FBcC method outperforms the EBcC method and achieves high retrieval efficiency over other recent methods in the literature for tests on three different databases, the affine shape database, the MPEG-7 database CE-1 part B and the Kimia's database.