TY - CONF T1 - Domain adaptation for object recognition: An unsupervised approach T2 - 2011 IEEE International Conference on Computer Vision (ICCV) Y1 - 2011 A1 - Gopalan,R. A1 - Ruonan Li A1 - Chellapa, Rama KW - Data models KW - data representations KW - discriminative classifier KW - Feature extraction KW - Grassmann manifold KW - image sampling KW - incremental learning KW - labeled source domain KW - Manifolds KW - measurement KW - object category KW - Object recognition KW - Principal component analysis KW - sampling points KW - semisupervised adaptation KW - target domain KW - underlying domain shift KW - unsupervised approach KW - unsupervised domain adaptation KW - Unsupervised learning KW - vectors AB - Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that is receiving recent attention. In this paper, we present one of the first studies on unsupervised domain adaptation in the context of object recognition, where we have labeled data only from the source domain (and therefore do not have correspondences between object categories across domains). Motivated by incremental learning, we create intermediate representations of data between the two domains by viewing the generative subspaces (of same dimension) created from these domains as points on the Grassmann manifold, and sampling points along the geodesic between them to obtain subspaces that provide a meaningful description of the underlying domain shift. We then obtain the projections of labeled source domain data onto these subspaces, from which a discriminative classifier is learnt to classify projected data from the target domain. We discuss extensions of our approach for semi-supervised adaptation, and for cases with multiple source and target domains, and report competitive results on standard datasets. JA - 2011 IEEE International Conference on Computer Vision (ICCV) PB - IEEE SN - 978-1-4577-1101-5 M3 - 10.1109/ICCV.2011.6126344 ER -