NDLI: Multi-feature based 3D model similarity retrieval

Content Provider	IEEE Xplore Digital Library
Author	Akbar, S. Kueng, J. Wagner, R.
Copyright Year	2006
Description	Author affiliation: FAW, Johannes Kepler Univ. of Linz, Linz, Austria (Akbar, S.; Kueng, J.; Wagner, R.)
Abstract	This paper presents an approach to measure 3D similarity by combining two feature vectors. We extract the feature vectors by employing two similarity models: direction vector of surfaces (DVS) and shape histogram of projected volume (SHV). Then we merge the features by two approaches: merging the two original feature vectors and merging computed-distances. Our experiments show that combining two features using either feature merging or distance merging enhances the retrieval performance. Furthermore, we show that employing weighting factor to the merging process implies differently to the retrieval performance, depending on data set distribution. Finally, we introduce an idea of meta feature-vectors which regards the already calculated distances as new feature vectors. Using this approach, a new similarity space might be established, and new distances could be calculated in order to enhance the performance.
Starting Page	1
Ending Page	6
File Size	1359730
Page Count	6
File Format	PDF
ISBN	9781424402199
DOI	10.1109/ICOCI.2006.5276461
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-06
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Geometry Solid modeling Histograms Shape Biological system modeling Merging Virtual reality Feature extraction Power system modeling Context modeling
Content Type	Text
Resource Type	Article

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