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| Content Provider | ACM Digital Library |
|---|---|
| Author | Vandeborre, Jean Philippe Daoudi, Mohamed Tabia, Hedi Colot, Olivier |
| Abstract | We present a novel method for 3D-object retrieval using Bag of Feature (BoF) approaches [8]. The method starts by selecting and then describing a set of points from the 3D-object. The proposed descriptor is an indexed collection of closed curves in $R^{3}$ on the 3D-surface. Such descriptor has the advantage of being invariant to different transformations that a shape can undergo. Based on vector quantization, we cluster those descriptors to form a shape vocabulary. Then, each point selected in the object is associated to a cluster (word) in that vocabulary. Finally, a BoF histogram counting the occurrences of every word is computed. In order to assess our method, we used shapes from the TOSCA and Sumner datasets. The results clearly demonstrate that the method is robust to many kind of transformations and produces higher precision compared with some state-of-the-art methods. |
| Starting Page | 3 |
| Ending Page | 8 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450301602 |
| DOI | 10.1145/1877808.1877811 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2010-10-25 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Bag of features Retrieval Curve analysis 3d shape |
| Content Type | Text |
| Resource Type | Article |
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