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.
| Original language | English |
|---|---|
| Title of host publication | 1st International Conference on Digital Information Management (ICDIM), India, December 2006 |
| Number of pages | 6 |
| Publication status | Published - Dec 2006 |
Fields of science
- 102001 Artificial intelligence
- 102006 Computer supported cooperative work (CSCW)
- 102010 Database systems
- 102014 Information design
- 102015 Information systems
- 102016 IT security
- 102028 Knowledge engineering
- 102019 Machine learning
- 102022 Software development
- 102025 Distributed systems
- 502007 E-commerce
- 505002 Data protection
- 506002 E-government
- 509018 Knowledge management
- 202007 Computer integrated manufacturing (CIM)
- 102033 Data mining
- 102035 Data science
