Name
Abstract | ||
|---|---|---|
Training a 3D model classifier on a small dataset is very challenging. However, large datasets of partially classified models are now commonly available online. We use an external training set of models with associated text tags to automatically assign tags to both training and query models. The similarity between these tags, used in conjunction with a standard shape descriptor, yields a multiclassifier that outperforms the standalone shape descriptor. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/SMI.2008.4547983 | IEEE INTERNATIONAL CONFERENCE ON SHAPE MODELING AND APPLICATIONS 2008, PROCEEDINGS |
Keywords | Field | DocType |
classification, labeling, autotagging. | Training set,Text mining,Pattern recognition,Computer science,Search engine indexing,Artificial intelligence,Classifier (linguistics),Robotics | Conference |
Citations | PageRank | References |
3 | 0.69 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Corey Goldfeder | 1 | 279 | 15.95 |
| Haoyun Feng | 2 | 4 | 1.41 |
| Peter K. Allen | 3 | 3089 | 268.12 |