Extreme Curvature Scale Space for Efficient
Shape Similarity Retrieval
Hassan Silkan1, Said Ouatik2, and Abdelmounaime Lachkar2
1Department
of Computer Science, Chouaib Doukkali University, Morocco
2Department of Electrical and Computer
Engineering, Sidi Mohmmed Ben
Abdellah University, Morocco
Abstract: The description of the object shape is an important characteristic
of the image; several different shape descriptors are used. This paper presents
a novel shape descriptor which is robust with respect to noise, scale and
orientation changes of the objects. It is based on the multi scale space
approach to identify shapes. The descriptor of a shape is created by tracking
the position of extreme curvature points in a shape boundary filtered by
low-pass Gaussian filters of variable widths. The result of this process is a
several contours map representing the extreme curvature points of the shape as
it is smoothed. The maxima of these contours are used to represent a shape. We
demonstrate object recognition for three data sets, a classified subset of
database SQUID, the set of silhouettes from the MPEG-7 database and the set of
2D views of 3D objects from the Columbia Object Image Library (COIL-100)
database. The results prove the performance and robustness of the developed
method and its superiority over Curvature Scale Space (CSS) in shape with
shallow concavities.
Keywords: Multi scale analysis, CSS, shape
similarity, image database retrieval.