Topic models for image localization
Date
2013-08-01
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Abstract
We present a new scheme for partitioning geo-tagged reference image database in an effort
to speed up query image localization while maintaining acceptable localization accuracy. Our
method learns a topic model over the reference database, which in turn is used to divide the
reference database into scene groups. Each scene group consists of “visually similar” images
as determined by the topic model. Next raw SIFT features are collected from every image
in a scene group and a FLANN index is constructed. Given a query image, first its scene
group is determined using the topic model and next its SIFT features are matched against
the corresponding FLANN index. The query image is localized using the location information
associated with the visually similar images in the reference database. We evaluate our approach
on Google Map Street View dataset and demonstrate that our method outperforms a competing
technique.
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Keywords
Image localization, Topic models, SIFT, FLANN, Visual words