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.
| Original language | English |
|---|---|
| Title of host publication | International Conference on Computing and Informatics, ICOCI 2006, Malaysia, June 2006 |
| Number of pages | 6 |
| Publication status | Published - Jun 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
