Local Signature Quantization by Sparse Coding

In 3D object retrieval it is very important to define reliable shape descriptors, which compactly characterize geometric properties of the underlying surface. To this aim two main approaches are considered: global, and local ones. Global approaches are effective in describing the whole object, while local ones are more suitable to characterize small parts of the shape. Some strategies to combine these two approaches have been proposed recently but still no consolidate work is available in this field. With this paper we address this problem and propose a new method based on sparse coding techniques. A set of local shape descriptors are collected from the shape. Then a dictionary is trained as generative model. In this fashion the dictionary is used as global shape descriptor for shape retrieval purposes. Preliminary experiments are performed on a standard dataset by showing a drastic improvement of the proposed method in comparison with well known local-to-global and global approaches.