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  1. Proceedings of the 3rd ACM SIGMM international workshop on Social media (WSM '11)
  2. IM2MAP: deriving maps from georeferenced community contributed photo collections
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Mining social media: issues and challenges
Using social media to identify events
Large-scale social media mining in facebook
Multimodal location estimation on Flickr videos
Photo stream alignment for collaborative photo collection and sharing in social media
Sentiment analysis of social media content using N-Gram graphs
IM2MAP: deriving maps from georeferenced community contributed photo collections
Near2me: an authentic and personalized social media-based recommender for travel destinations
Laplacian adaptive context-based SVM for video concept detection
Tag suggestion and localization for web videos by bipartite graph matching
Why do we converse on social media?: an analysis of intrinsic and extrinsic network factors
Chase display of social live streams (SOLISs)

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IM2MAP: deriving maps from georeferenced community contributed photo collections

Content Provider ACM Digital Library
Author Xie, Ling Newsam, Shawn
Abstract This paper investigates georeferenced social multimedia for geographic discovery. We propose a novel framework wherein large collections of community contributed photo collections are used to map phenomena not easily observable through other means. We employ a regression framework in which a limited number of labeled training images are used to learn a regressor. This regressor is then applied to large collections of novel images whose locations are known and the predictions are used to create maps. We propose two novel extensions to a standard regression approach. In the first, a graph Laplacian semi-supervised learning approach leverages unlabeled images to improve the accuracy of the regressor. This is important because it allows us to exploit large collections of community contributed photos while limiting the number of images that need to be manually labeled. In the second extension, the regressor is based on a novel composite visual-geographic location kernel which considers both the visual characteristics and the geographic locations of images. We apply our approach to predict the scenicness of geographic locations at the country-scale based on the ground-level photos at the locations. While our results are noisy, this preliminary investigation demonstrates the feasibility of geographic discovery from georeferenced social media as well as the advantages provided by our extensions to a standard regression approach.
Starting Page 29
Ending Page 34
Page Count 6
File Format PDF
ISBN 9781450309899
DOI 10.1145/2072609.2072620
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2011-11-30
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Composite kernels Georeferenced photo collections
Content Type Text
Resource Type Article
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