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Autotagging to improve text search for 3D models

Corey Goldfeder; Peter K. Allen

Title:
Autotagging to improve text search for 3D models
Author(s):
Goldfeder, Corey
Allen, Peter K.
Date:
Type:
Articles
Department:
Computer Science
Permanent URL:
Book/Journal Title:
IEEE International Conference on Shape Modeling and Applications 2008: Stony Brook, New York, USA, June 4-6, 2008: Proceedings
Book Author:
Spagnuolo, Michela
Publisher:
IEEE
Publisher Location:
Piscataway, N.J.
Abstract:
Text search on databases of 3D models has traditionally worked poorly, as text annotations on 3D models are often unreliable or incomplete. We attempt to improve the recall of text search by automatically assigning appropriate tags to models. Our algorithm finds relevant tags by appealing to a large corpus of partially labeled example models, which does not have to be preclassified or otherwise prepared. For this purpose we use a copy of Google 3D Warehouse, a database of user contributed models which is publicly available on the Internet. Given a model to tag, we find geometrically similar models in the corpus, based on distances in a reduced dimensional space derived from Zernike descriptors. The labels of these neighbors are used as tag candidates for the model with probabilities proportional to the degree of geometric similarity. We show experimentally that text based search for 3D models using our computed tags can approach the quality of geometry based search.
Subject(s):
Robotics
Artificial intelligence
Publisher DOI:
http://dx.doi.org/0.1109/SMI.2008.4548007
Item views:
78
Metadata:
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