This paper presents a novel approach for sketch-based image retrieval based on low-level features. It enables the measuring of the similarity among full color multi-component images within a database (models) and simple black and white user sketched queries. It needs no cost intensive image segmentation. Strong edges of the model image and morphologically thinned version of the query image are used for image abstraction. Angular-radial decomposition of pixels in the abstract images is used to extract new compact and affine invariant features. Comparative results, employing an art database (ArT BANK), show significant improvement in average normalized modified retrieval rank (ANMRR) using the proposed features.