NDLI: Multi-feature Integration on 3D Model Similarity Retrieval

Content Provider	IEEE Xplore Digital Library
Author	Akbar, S. Josef Kung Wagner, R.
Copyright Year	2006
Description	Author affiliation: Johannes Kepler Univ. of Linz, Linz (Akbar, S.)
Abstract	In this paper, we describe several 3D shape descriptors for 3D model retrieval and integrate them in order to obtain higher performance than single descriptor may yield. We analyze four feature vector (FV) integration approaches: Pure FV Integration (PFI), Reduced FV Integration (RFI), Distance Integration (DI), and Rank Integration (RI). We observe which weighting factor might be the best for each approach. Our experiments show that the weighting factors consistently enhance the retrieval performance on not only training dataset, but also another extended dataset. Our experiments also highlight that RFI, which is obviously useful for processing unknown query object, is the best among the others. In another side, DI provides faster processing as it uses pre-computed distance, but does not have a capability of processing unknown query object. Hence, both approaches could be combined in order to obtain higher efficiency and effectiveness of 3D model retrieval system for either known or unknown query object.
Starting Page	151
Ending Page	156
File Size	256916
Page Count	6
File Format	PDF
ISBN	142440682X
DOI	10.1109/ICDIM.2007.369345
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-06
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Biological system modeling Virtual reality Information retrieval Data mining Automobiles Virtual manufacturing Power system modeling Radiofrequency interference Computational biology
Content Type	Text
Resource Type	Article

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