z-logo
open-access-imgOpen Access
Feature-based similarity search in 3D object databases
Author(s) -
Benjamín Bustos,
Daniel A. Keim,
Dietmar Saupe,
Tobias Schreck,
D.V. Vranić
Publication year - 2005
Publication title -
acm computing surveys
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.079
H-Index - 163
eISSN - 1557-7341
pISSN - 0360-0300
DOI - 10.1145/1118890.1118893
Subject(s) - computer science , information retrieval , similarity (geometry) , nearest neighbor search , feature (linguistics) , multimedia database , object (grammar) , data mining , artificial intelligence , image (mathematics) , linguistics , philosophy
The development of effective content-based multimedia search systems is an important research issue due to the growing amount of digital audio-visual information. In the case of images and video, the growth of digital data has been observed since the introduction of 2D capture devices. A similar development is expected for 3D data as acquisition and dissemination technology of 3D models is constantly improving. 3D objects are becoming an important type of multimedia data with many promising application possibilities. Defining the aspects that constitute the similarity among 3D objects and designing algorithms that implement such similarity definitions is a difficult problem. Over the last few years, a strong interest in methods for 3D similarity search has arisen, and a growing number of competing algorithms for content-based retrieval of 3D objects have been proposed. We survey feature-based methods for 3D retrieval, and we propose a taxonomy for these methods. We also present experimental results, comparing the effectiveness of some of the surveyed methods.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom