TY - GEN
T1 - Meta-classifiers for multimodal document classification
AU - Chen, Scott Deeann
AU - Monga, Vishal
AU - Moulin, Pierre
PY - 2009
Y1 - 2009
N2 - This paper proposes learning algorithms for the problem of multimodal document classification. Specifically, we develop classifiers that automatically assign documents to categories by exploiting features from both text as well as image content. In particular, we use meta-classifiers that combine state-of-the-art text and image based classifiers into making joint decisions. The two meta classifiers we choose are based on support vector machines and Adaboost. Experiments on real-world databases from Wikipedia demonstrate the benefits of a joint exploitation of these modalities.
AB - This paper proposes learning algorithms for the problem of multimodal document classification. Specifically, we develop classifiers that automatically assign documents to categories by exploiting features from both text as well as image content. In particular, we use meta-classifiers that combine state-of-the-art text and image based classifiers into making joint decisions. The two meta classifiers we choose are based on support vector machines and Adaboost. Experiments on real-world databases from Wikipedia demonstrate the benefits of a joint exploitation of these modalities.
UR - http://www.scopus.com/inward/record.url?scp=74349088575&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=74349088575&partnerID=8YFLogxK
U2 - 10.1109/MMSP.2009.5293343
DO - 10.1109/MMSP.2009.5293343
M3 - Conference contribution
AN - SCOPUS:74349088575
SN - 9781424444649
T3 - 2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09
BT - 2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09
T2 - 2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09
Y2 - 5 October 2009 through 7 October 2009
ER -