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• W1942040090 abstract "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 the 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." @default.
• W1942040090 created "2016-06-24" @default.
• W1942040090 creator A5036016228 @default.
• W1942040090 date "2015-06-01" @default.
• W1942040090 modified "2023-09-27" @default.
• W1942040090 title "Hierarchical sparse coding with geometric prior for visual geo-location" @default.
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• W1942040090 doi "https://doi.org/10.1109/cvpr.2015.7298857" @default.
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