Abstract
In this paper we describe a probabilistic image matching scheme in which the image representation is continuous and the similarity measure and distance computation are also defined in the continuous domain. Each image is first represented as a Gaussian mixture distribution and images are compared and matched via a probabilistic measure of similarity between distributions. A common probabilistic and continuous framework is applied to the representation as well as the matching process, ensuring an overall system that is theoretically appealing. Matching results are investigated and the application to an image retrieval system is demonstrated.
| Original language | English |
|---|---|
| Pages (from-to) | 384-406 |
| Number of pages | 23 |
| Journal | Computer Vision and Image Understanding |
| Volume | 84 |
| Issue number | 3 |
| DOIs | |
| State | Published - Dec 2001 |
| Externally published | Yes |
Bibliographical note
Funding Information:Hayit Greenspan was supported by the Eshkol Grant of the Ministry of Science. Part of the work was supported by the Israeli Ministry of Science, Grant 05530462.
Funding
Hayit Greenspan was supported by the Eshkol Grant of the Ministry of Science. Part of the work was supported by the Israeli Ministry of Science, Grant 05530462.
| Funders | Funder number |
|---|---|
| Israeli Ministry of Science | 05530462 |
| Ministry of Science |
Keywords
- Gaussian mixture modeling
- Image matching
- Image representation
- Kullback-Leibler distance
- Probabilistic matching