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Hierarchical sparse coding with geometric prior for visual geo-location
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
We address the problem of estimating location information of an image using principles from automated representation learning. We pursue a hierarchical sparse coding approach that learns features useful in discriminating images across locations, by initializing it with a geometric prior corresponding to transformations between image appearance space and their corresponding location grouping space using the notion of parallel transport on manifolds. We then extend this approach to account for
doi:10.1109/cvpr.2015.7298857
dblp:conf/cvpr/Gopalan15
fatcat:aka4cafrgvekriqdsrscvxq2pi