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|Author(s):||P J. Phillips; Jingjing Zheng; Ming-Yu Liu; Rama Chellappa;|
|Title:||A Grassmann Manifold-based Domain Adaptation Approach|
|Published:||November 20, 2012|
|Abstract:||Domain adaptation algorithms that handle shifts in the distribution between training and testing data are receiving much attention in computer vision. Recently, a Grassmann manifold-based domain adaptation algorithm that models the domain shift using intermediate subspaces along the geodesic connecting the source and target domains was presented in . We build upon this work and propose replacing the step of concatenating feature projections on a very few sampled inter- mediate subspaces by directly integrating the distance between feature projections along the geodesic. The proposed approach considers all the intermediate sub- spaces along the geodesic. Thus, it is a more principled way of quantifying the cross-domain distance. We present the results of experiments on two standard datasets and show that the proposed algorithm yields favorable performance over previous approaches.|
|Conference:||International Conference on Pattern Recognition|
|Proceedings:||Proceedings of the International Conference on Pattern Recognition|
|Location:||Tuskuba Science City, -1|
|Dates:||November 11-15, 2012|
|PDF version:||Click here to retrieve PDF version of paper (1MB)|