NDLI: Multimodal Location Estimation of Consumer Media: Dealing with Sparse Training Data

Content Provider	IEEE Xplore Digital Library
Author	Jaeyoung Choi Friedland, G. Ekambaram, V. Ramchandran, K.
Copyright Year	2012
Abstract	This article describes a novel approach to the problem of associating geo-locations to consumer-produced multimedia data such as videos and photos that are publicly available on social networking websites such as Flickr. We specifically focus on the case where the available training data is sparse both in absolute numbers as well as geographic coverage when compared to the number of untagged query data. We develop a novel graphical model based framework for the problem of interest and pose the problem of geotagging as one of inference over this graph. The novelty of our algorithm lies in the fact that we jointly estimate the geo-locations of all the query videos, which helps obtain performance improvements over existing algorithms in the literature that process each query video independently. Our system enables the query videos to act as "virtual" training data that effectively bootstrap the geo-tagging process. The quality of the database improves with each additional query video in the system. Further, our modeling provides a generic theoretical framework that can be used to incorporate any other available textual, visual or audio features. We evaluate our algorithm on the MediaEval 2011 Placing Task data set and show that for fixed training data the system performance improves with an increasing number of unlabeled test data. The performance gains are shown to be over 10% as compared to existing algorithms in the literature.
Starting Page	43
Ending Page	48
File Size	724219
Page Count	6
File Format	PDF
ISBN	9781467316590
ISSN	19457871
DOI	10.1109/ICME.2012.141
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-09
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Videos Training Graphical models Training data Visualization Databases Random variables Belief Propagation Multimodal Location Estimation Geo-Tagging Graphical Models
Content Type	Text
Resource Type	Article

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