Empirical Inference
Conference Paper
2007
How to Find Interesting Locations in Video: A Spatiotemporal Interest Point Detector Learned from Human Eye movements
PDF Web
Empirical Inference
Interest point detection in still images is a well-studied topic in computer vision. In the spatiotemporal domain, however, it is still unclear which features indicate useful interest points. In this paper we approach the problem by emph{learning} a detector from examples: we record eye movements of human subjects watching video sequences and train a neural network to predict which locations are likely to become eye movement targets. We show that our detector outperforms current spatiotemporal interest point architectures on a standard classification dataset.
| Author(s): | Kienzle, W. and Schölkopf, B. and Wichmann, F. and Franz, MO. |
| Links: | |
| Book Title: | Pattern Recognition |
| Journal: | Pattern Recognition: 29th DAGM Symposium |
| Pages: | 405-414 |
| Year: | 2007 |
| Month: | September |
| Day: | 0 |
| Editors: | FA Hamprecht and C Schn{\"o}rr and B J{\"a}hne |
| Publisher: | Springer |
| BibTeX Type: | Conference Paper (inproceedings) |
| Address: | Berlin, Germany |
| DOI: | 10.1007/978-3-540-74936-3_41 |
| Event Name: | 29th Annual Symposium of the German Association for Pattern Recognition (DAGM 2007) |
| Event Place: | Heidelberg, Germany |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@inproceedings{4486,
title = {How to Find Interesting Locations in Video: A Spatiotemporal Interest Point Detector Learned from Human Eye movements},
journal = {Pattern Recognition: 29th DAGM Symposium},
booktitle = {Pattern Recognition},
abstract = {Interest point detection in still images is a well-studied topic in computer vision.
In the spatiotemporal domain, however, it is still unclear which features indicate useful interest points. In this paper we approach the problem by emph{learning} a detector from examples: we record eye movements of human subjects watching video sequences and train a neural network to predict which locations are likely to become eye movement targets. We show that our detector outperforms current spatiotemporal interest point architectures on a standard classification dataset.},
pages = {405-414},
editors = {FA Hamprecht and C Schn{\"o}rr and B J{\"a}hne},
publisher = {Springer},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {Berlin, Germany},
month = sep,
year = {2007},
author = {Kienzle, W. and Sch{\"o}lkopf, B. and Wichmann, F. and Franz, MO.},
doi = {10.1007/978-3-540-74936-3_41},
month_numeric = {9}
}