How flickr helps us make sense of the world: context and content in community-contributed media collections

By L. Kennedy, Mor Naaman, Shane Ahern, Rahul Nair, Tye Rattenbury. ACM Multimedia, 2007

428 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/de4cf7f5c5a85cfae4deb9f95408f96b3e68b728

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In Computer Vision and Pattern Recognition

Citations in the corpus

Harvesting ambient geospatial information from social media feeds (2011, background): “Taking advantage of the annotation content of these datasets provides new analysis opportunities, ranging from (Kennedy et al. 2007), and even identifying events and corresponding social media documents (Becker et al. 2010).”
Learning similarity metrics for event identification in social media (2010, methodology): “Tags have also been used in conjunction with other context [25] to retrieve Flickr images of geographical landmarks.”
Content-based visual search learned from social media (2012, background): “A number of applications are grounded on social image search, such as landmark visualization [55], visual query suggestion [147], training data acquisition [115], photo-based question answering [144], and photo-based advertisements [70], to name a few.”
Extracting and understanding urban areas of interest using geotagged photos (2015, methodology): “There are also many other interesting applications of Flickr data, such as event extraction (Kennedy et al. 2007) and movement pattern analysis (Jankowski et al. 2010).”
A Tale of Cities: Urban Biases in Volunteered Geographic Information (2014, background): “Flickr tags have been used in a large variety of contexts ranging from identifying representative labels for a given region (e.g. Kennedy et al. 2007; Moxley, Kleban, and Manjunath 2008 ) to mining vernacular (i.e. " colloquial " ) regions (Thomee and Rae 2013) to georeferencing photographs without…”
Travel route recommendation using geotags in photo sharing sites (2010, background): “…spatial information into Web applications; recent research efforts have shown the potential of geotags by developing various geotag-associated applications such as georeferenced image search [7, 8], automatic image geolocation [9, 10, 11], and georeferenced content browsing [15, 16, 17, 18, 19].”
Web-Scale Image Clustering Revisited (2015, background): “[19] were among the first to extract textual and geographical patterns from metadata and use them in visual clustering.”
Generating diverse and representative image search results for landmarks (2008, background): “The current work exceeds and extends [11], which gave a general overview of the system and did not supply the details of the visual analysis, or the deeper evaluation we perform here.”
Retrieving landmark and non-landmark images from community photo collections (2010, methodology): “For geo-clustering, [15] and [36] use the same al-gorithm as for visual clustering, whereas [27] and [8] simply quantize locations into overlapping rectangular tiles.”
Multimodal Fusion for Video Search Reranking (2010, background): “’s work [22], where a similar structure to [21] is smartly exploited to build a vivid pictorial map of the world from the user-shared multimedia resources.”