Abstract
This chapter describes an application using established classification techniques for performing simple multimodal location estimation experiments. It demonstrates the use of Gaussian Mixture Model (GMM)—and language model-based approaches for verifying the cities from which Flickr videos are taken based on the videos’ audio and textual metadata. The methods used in most of the approaches are described in detail, allowing people with no background in location estimation to perform simple experiments. The city-verification results for the approaches are not eye-popping by any means, but are above-random and present opportunities for future work in the development of better approaches. The techniques may also be suitable for class projects, for students who wish to gain hands-on experience in performing location estimation.
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Acknowledgments
The experiments described in this work were supported by NGA NURI grant number HM11582-10-1-0008, NSF EAGER grant IIS-1138599, and NSF Award CNS-1065240. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the sponsors.
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Lei, H., Choi, J., Friedland, G. (2015). Application of Large-Scale Classification Techniques for Simple Location Estimation Experiments. In: Choi, J., Friedland, G. (eds) Multimodal Location Estimation of Videos and Images. Springer, Cham. https://doi.org/10.1007/978-3-319-09861-6_6
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DOI: https://doi.org/10.1007/978-3-319-09861-6_6
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