Hierarchical sparse coding with geometric prior for visual geo-location

Raghuraman Gopalan
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
more » ... availability of heterogeneous data modalities such as geo-tags and videos pertaining to different locations, and also study a relatively under-addressed problem of transferring knowledge available from certain locations to infer the grouping of data from novel locations. We evaluate our approach on several standard datasets such as im2gps, San Francisco and MediaEval2010, and obtain state-of-the-art results.
doi:10.1109/cvpr.2015.7298857 dblp:conf/cvpr/Gopalan15 fatcat:aka4cafrgvekriqdsrscvxq2pi