A salient-point signature for 3d object retrieval

Authors:
Indriyati Atmosukarto;Linda G. Shapiro
Affiliations:
University of Washington, Seattle, WA, USA;University of Washington, Seattle, WA, USA
Venue:
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Year:
2008

Citing 9
Cited 3

Mesh saliency

ACM SIGGRAPH 2005 Papers
Symbolic Signatures for Deformable Shapes

IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based retrieval of 3D models

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Content-based retrieval of 3D models through curvature maps: a CBR approach exploiting media conversion

Multimedia Tools and Applications
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Extracting 3D Shape Features in Discrete Scale-Space

3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Content-Based 3D Object Retrieval

IEEE Computer Graphics and Applications
A Learning Approach to 3D Object Representation for Classification

SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Three-dimensional shape searching: state-of-the-art review and future trends

Computer-Aided Design

3D object classification using salient point patterns with application to craniofacial research

Pattern Recognition
3d object retrieval using salient views

Proceedings of the international conference on Multimedia information retrieval
Evaluation of 3D interest point detection techniques

EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we describe a new 3D object signature and evaluate its performance for 3D object retrieval. The signature is based on a learning approach that finds the characteristics of salient points on a 3D object and represents the points in a 2D spatial map based on a longitude-latitude transformation. Experimental results show that the signature is able to achieve good retrieval scores for both pose-normalized and randomly-rotated object queries.