A comparative study of Motion Descriptors and Zernike moments in color object recognition

Classification and object recognition is one of the most important tasks in image processing. Most applications deal with the classification of definite shapes, for example identifying a particular type aircraft. In these ap plications, compact visual descriptors are necessary to describe image content . Fourier descriptors are widely used in image proc essing to describe and classify object. Several techniques have proved useful moment's invariants. In this paper, we studied Motion de scriptors (MD) introduced recently by Gauthier et al.; combined with Zernike Moments (ZM). Experiments are conducted using three databases: COIL-100, which consists of 3D objects, A R faces and cellular phones database. Recognition is performed by a Supp ort Vector Machine as supervised classification method.