Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | ISM 2006 |
| Untertitel | 8th IEEE International Symposium on Multimedia |
| Seiten | 78-85 |
| Seitenumfang | 8 |
| Publikationsstatus | Veröffentlicht - 2006 |
| Extern publiziert | Ja |
| Veranstaltung | ISM 2006 - 8th IEEE International Symposium on Multimedia - San Diego, CA, USA / Vereinigte Staaten Dauer: 11 Dez. 2006 → 13 Dez. 2006 |
Publikationsreihe
| Name | ISM 2006 - 8th IEEE International Symposium on Multimedia |
|---|
Abstract
Retrieving information about the occurrences of persons in a video is an important task in many video indexing and retrieval applications. The problem is to answer the question "In which shots and scenes does person X appear?". In this paper, we present an automatic video annotation system with respect to a person's appearance based on state-of-the-art algorithms for face detection, tracking and recognition. In contrast to many related approaches, knowledge about the persons in a given video is not assumed in advance. Adaboost is employed after an initial clustering of faces to select the best features describing a person's face. These features are then used to train new classifiers based only on the faces extracted from the video under consideration. Several possibilities to train Adaboost and Support Vector Machine (ensemble) classifiers directly on a video are compared. Finally, experimental results demonstrate the effectiveness of correcting in-plane face rotation and of the employed self-supervised learning method.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computernetzwerke und -kommunikation
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ISM 2006 : 8th IEEE International Symposium on Multimedia. 2006. S. 78-85 4061154 (ISM 2006 - 8th IEEE International Symposium on Multimedia).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Self-supervised learning of face appearances in TV casts and movies
AU - Ewerth, Ralph
AU - Mühling, Markus
AU - Freisleben, Bernd
PY - 2006
Y1 - 2006
N2 - Retrieving information about the occurrences of persons in a video is an important task in many video indexing and retrieval applications. The problem is to answer the question "In which shots and scenes does person X appear?". In this paper, we present an automatic video annotation system with respect to a person's appearance based on state-of-the-art algorithms for face detection, tracking and recognition. In contrast to many related approaches, knowledge about the persons in a given video is not assumed in advance. Adaboost is employed after an initial clustering of faces to select the best features describing a person's face. These features are then used to train new classifiers based only on the faces extracted from the video under consideration. Several possibilities to train Adaboost and Support Vector Machine (ensemble) classifiers directly on a video are compared. Finally, experimental results demonstrate the effectiveness of correcting in-plane face rotation and of the employed self-supervised learning method.
AB - Retrieving information about the occurrences of persons in a video is an important task in many video indexing and retrieval applications. The problem is to answer the question "In which shots and scenes does person X appear?". In this paper, we present an automatic video annotation system with respect to a person's appearance based on state-of-the-art algorithms for face detection, tracking and recognition. In contrast to many related approaches, knowledge about the persons in a given video is not assumed in advance. Adaboost is employed after an initial clustering of faces to select the best features describing a person's face. These features are then used to train new classifiers based only on the faces extracted from the video under consideration. Several possibilities to train Adaboost and Support Vector Machine (ensemble) classifiers directly on a video are compared. Finally, experimental results demonstrate the effectiveness of correcting in-plane face rotation and of the employed self-supervised learning method.
UR - http://www.scopus.com/inward/record.url?scp=36849088984&partnerID=8YFLogxK
U2 - 10.1109/ISM.2006.136
DO - 10.1109/ISM.2006.136
M3 - Conference contribution
AN - SCOPUS:36849088984
SN - 0769527469
SN - 9780769527468
T3 - ISM 2006 - 8th IEEE International Symposium on Multimedia
SP - 78
EP - 85
BT - ISM 2006
T2 - ISM 2006 - 8th IEEE International Symposium on Multimedia
Y2 - 11 December 2006 through 13 December 2006
ER -