 |
Technical Program
Paper Detail
| Paper: | WP-P5.7 |
| Session: | Image/Video Storage and Retrieval |
| Time: | Wednesday, October 11, 14:20 - 17:00 |
| Presentation: |
Poster
|
| Title: |
PRECISION-ORIENTED ACTIVE SELECTION FOR INTERACTIVE IMAGE RETRIEVAL |
| Authors: |
Philippe Henri Gosselin; ETIS CNRS UMR 8051 | | | | | Matthieu Cord; ETIS CNRS UMR 8051 | | |
| Abstract: |
Active learning methods have been considered with an increased interest in the content-based image retrieval (CBIR) community. These methods have been developped for classification problems, and do not deal with the particular characteristics of the CBIR. One of these characteristics is the criterion to optimize, for instance the error of generalization for classification, which is not the best adapted to CBIR context. We introduce in this paper an active selection which chooses the image the user should label such as the Mean Average Precision is increased. The method is smartly combined with previous propositions, and leads to a fast and efficient active learning scheme. Experiments on a large database have been carried out in order to compare our approach to several other methods. |
|
