In 3D object retrieval it is very important to define reliable shape descriptors, which compactly characterizegeometric properties of the underlying surface. To this aim two main approaches are considered: global, andlocal ones. Global approaches are effective in describing the whole object, while local ones are more suitableto characterize small parts of the shape. Some strategies to combine these two approaches have been proposedrecently but still no consolidate work is available in this field. With this paper we address this problem and proposea 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 descriptorfor shape retrieval purposes. Preliminary experiments are performed on a standard dataset by showing a drasticimprovement of the proposed method in comparison with well known local-to-global and global approaches.

Local signature quantization by sparse coding

CASTELLANI, Umberto
2013

Abstract

In 3D object retrieval it is very important to define reliable shape descriptors, which compactly characterizegeometric properties of the underlying surface. To this aim two main approaches are considered: global, andlocal ones. Global approaches are effective in describing the whole object, while local ones are more suitableto characterize small parts of the shape. Some strategies to combine these two approaches have been proposedrecently but still no consolidate work is available in this field. With this paper we address this problem and proposea 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 descriptorfor shape retrieval purposes. Preliminary experiments are performed on a standard dataset by showing a drasticimprovement of the proposed method in comparison with well known local-to-global and global approaches.
3D object retrieval; Sparse Coding; Diffusion Geometry
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11562/668363
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