Abstract
This paper studies the problem of estimating geographical locations of images. To build reliable geographical estimators, an important question is to find distinguishable geographical clusters in the world. Those clusters cover general geographical regions and are not limited to landmarks. The geographical clusters provide more training samples and hence lead to better recognition accuracy. Previous approaches build geographical clusters using heuristics or arbitrary map grids, and cannot guarantee the effectiveness of the geographical clusters. This paper develops a new framework for geographical cluster estimation, and employs latent variables to estimate the geographical clusters. To solve this problem, this paper employs the recent progress in object detection, and builds an efficient solver to find the latent clusters. The results on beach datasets validate the success of our method.
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Wang, Y., Cao, L. (2013). Discovering Latent Clusters from Geotagged Beach Images. In: Li, S., et al. Advances in Multimedia Modeling. Lecture Notes in Computer Science, vol 7733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35728-2_13
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DOI: https://doi.org/10.1007/978-3-642-35728-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35727-5
Online ISBN: 978-3-642-35728-2
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