A Grassmann Manifold-based Domain Adaptation Approach

    •  Zheng, J.; Liu, M.-Y.; Chellappa, R.; Phillips, P.J., "A Grassmann Manifold-based Domain Adaptation Approach", IEEE International Conference on Pattern Recognition (ICPR), November 2012.
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      • @inproceedings{Zheng2012nov,
      • author = {Zheng, J. and Liu, M.-Y. and Chellappa, R. and Phillips, P.J.},
      • title = {A Grassmann Manifold-based Domain Adaptation Approach},
      • booktitle = {IEEE International Conference on Pattern Recognition (ICPR)},
      • year = 2012,
      • month = nov,
      • url = {}
      • }
  • Research Area:

    Computer Vision

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 [6]. We build upon this work and propose replacing the step of concatenating feature projections on a very few sampled intermediate subspaces by directly integrating the distance between feature projections along the geodesic. The proposed approach considers all the intermediate subspaces 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.