TY  - CONF
AU  - Jiaolong Xu
AU  - Sebastian Ramos
AU  - David Vazquez
AU  - Antonio Lopez
A2  - ICPR
PY  - 2014//
TI  - Cost-sensitive Structured SVM for Multi-category Domain Adaptation
BT  - 22nd International Conference on Pattern Recognition
SP  - 3886
EP  - 3891
PB  - IEEE
KW  - Domain Adaptation
KW  - Pedestrian Detection
N2  - Domain adaptation addresses the problem of accuracy drop that a classifier may suffer when the training data (source domain) and the testing data (target domain) are drawn from different distributions. In this work, we focus on domain adaptation for structured SVM (SSVM). We propose a cost-sensitive domain adaptation method for SSVM, namely COSS-SSVM. In particular, during the re-training of an adapted classifier based on target and source data, the idea that we explore consists in introducing a non-zero cost even for correctly classified source domain samples. Eventually, we aim to learn a more targetoriented classifier by not rewarding (zero loss) properly classified source-domain training samples. We assess the effectiveness of COSS-SSVM on multi-category object recognition.
SN  - 1051-4651
L1  - http://refbase.cvc.uab.es/files/xrv2014.pdf
UR  - http://dx.doi.org/10.1109/ICPR.2014.666
N1  - ADAS; 600.057; 600.054; 601.217; 600.076
ID  - Jiaolong Xu2014
ER  -