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A Novel Fusion Method for Integrating Multiple Modalities and Knowledge for Multimodal Location Estimation
Citation key 1427Kelm2013
Author Pascal Kelm and Sebastian Schmiedeke and Jaeyoung Choi and Gerald Friedland and Venkatesan Nallampatti Ekambaram and Kannan Ramchandran and Thomas Sikora
Title of Book Proceedings of the 2nd ACM International Workshop on Geotagging and Its Applications in Multimedia
Pages 7–12
Year 2013
DOI 10.1145/2509230.2509238
Address Barcelona, Spain
Month oct
Note isbn = 978-1-4503-2391-8 acmid = 2509238
Publisher ACM, New York, NY, USA
Abstract This article describes a novel fusion approach for multiple modalities and knowledge sources that improves the accuracy of multimodal location estimation algorithms. The problem of "multimodal location estimation" or "placing" consists of associating geo-locations to consumer-produced multimedia data such as videos or photos that have not been tagged using GPS. Our algorithm effectively integrates the visual and textual modalities with external geographical knowledge bases by building a hierarchical model that combines both data-driven as well as semantic methods to group visual and textual features together into geographical regions. We evaluate our algorithm on the MediaEval 2010 Placing Task data set and show that our system outperforms the state of the art significantly, achieving to locate about 40% of the videos within an accuracy radius of 100m.
Link to publication [1] Download Bibtex entry [2]
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