Abstract
Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources. However, in data analysis tasks such as classification and novelty detection, appropriate statistical models for point pattern data have not received much attention. This paper proposes the modelling of point pattern data via random finite sets (RFS). In particular, we propose appropriate likelihood functions, and a maximum likelihood estimator for learning a tractable family of RFS models. In novelty detection, we propose novel ranking functions based on RFS models, which substantially improve performance.
| Original language | English |
|---|---|
| Title of host publication | 2016 23rd International Conference on Pattern Recognition (ICPR 2016) |
| Subtitle of host publication | Cancún Center, Cancún, México, December 4-8, 2016 |
| Editors | Larry Davis, Alberto Del Bimbo, Brian C. Lovell |
| Place of Publication | Piscataway NJ USA |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 2622-2627 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509048472 |
| DOIs | |
| Publication status | Published - 2016 |
| Externally published | Yes |
| Event | International Conference on Pattern Recognition 2016 - Cancun, Mexico Duration: 4 Dec 2016 → 8 Dec 2016 Conference number: 23rd http://www.icpr2016.org/site/ https://ieeexplore.ieee.org/xpl/conhome/7893644/proceeding (Proceedings) |
Conference
| Conference | International Conference on Pattern Recognition 2016 |
|---|---|
| Abbreviated title | ICPR 2016 |
| Country/Territory | Mexico |
| City | Cancun |
| Period | 4/12/16 → 8/12/16 |
| Internet address |
Keywords
- Classification
- Multiple instance data
- Naive Bayes model
- Novelty detection
- Point pattern data
- Point process
- Random finite set