HSD: A 3D shape descriptor based on the Hilbert curve and a reduction dimensionality approach
Similarity searching based on 3D shape descriptors is an important process in content-based 3D shape retrieval tasks. The development of efficient 3D shape descriptors is still a challenge. This paper proposes a novel approach to characterize 3D shapes that is based on a Hilbert curve for scanning t...
Gespeichert in:
Hauptverfasser: | , , , |
---|---|
Format: | Tagungsbericht |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Similarity searching based on 3D shape descriptors is an important process in content-based 3D shape retrieval tasks. The development of efficient 3D shape descriptors is still a challenge. This paper proposes a novel approach to characterize 3D shapes that is based on a Hilbert curve for scanning the volume, dimensionality reduction by discrete wavelet transform and artificial neural networks. Our proposal, called Hilbert based 3D-shape Description, yields a high level descriptor that preserves the relevant characteristics of a 3D shape. Our proposal is invariant under translation transformation and it is robust under scale transformation. The experiments were carried out using the Princeton Shape Benchmark. The evaluation of the results indicated a higher precision rate, when compared to the competitive works. |
---|---|
ISSN: | 1062-922X |
DOI: | 10.1109/ICSMC.2012.6377693 |