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Flickr is included as keyword or extra keyword in 0 datasets, 0 tools and 8 publications.
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|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Web image retrieval re-ranking with Wikipedia semantics||Seungwoo Lee
|International Journal of Multimedia and Ubiquitous Engineering||English||2012||Nowadays, to take advantage of tags is a general tendency when users need to store or retrieve images on the Web. In this article, we introduce some approaches to calculate semantic importance of tags attached to Web images, and to make re-ranking the retrieved images according to them. We have compared the results from image re-ranking with two semantic providers, Word Net and Wikipedia. With the semantic importance of image tags calculated by using Wikipedia, we found the superiority of the method in precision and recall rate as experimental results.||0||0|
|Oh! Web 2.0, Virtual Reference Service 2.0, Tools & Techniques (I): A Basic Approach||Arya H.B.
|Journal of Library and Information Services in Distance Learning||English||2011||This study targets librarians and information professionals who use Web 2.0 tools and applications with a view to providing snapshots on how Web 2.0 technologies are used. It also aims to identify values and impact that such tools have exerted on libraries and their services, as well as to detect various issues associated with the implementation of Web 2.0 applications in libraries. Offering Web 2.0 tools and technologies to library patrons is also suggested.||0||0|
|Remixing excess capacity||Chase R.||Journal of Urban Regeneration and Renewal||English||2011||Excess capacity is a free resource, already paid for and in place. Thinking creatively about what constitutes excess capacity and tapping into it can result in fast, powerful, significant, and low-cost innovation and value. Governments, organisations and individuals should think creatively about their assets (physical assets, online data, networks, experiences and expertise) and find ways they can tap into, open up and repurpose it. Examples cites include Wikipedia, Flickr, Cyclovia, Couchsurfing, AirBnB, Shiply, AppsForDemcracy, Open SF, MassDotDev, Braddock Pennsylvania, Antoine Dodson and Auto-Tune the News.||0||0|
|Social media driven image retrieval||Adrian Popescu
|Proceedings of the 1st ACM International Conference on Multimedia Retrieval, ICMR'11||English||2011||People often try to find an image using a short query and images are usually indexed using short annotations. Matching the query vocabulary with the indexing vocabulary is a difficult problem when little text is available. Textual user generated content in Web 2.0 platforms contains a wealth of data that can help solve this problem. Here we describe how to use Wikipedia and Flickr content to improve this match. The initial query is launched in Flickr and we create a query model based on co-occurring terms. We also calculate nearby concepts using Wikipedia and use these to expand the query. The final results are obtained by ranking the results for the expanded query using the similarity between their annotation and the Flickr model. Evaluation of these expansion and ranking techniques, over the Image CLEF 2010 Wikipedia Collection containing 237,434 images and their multilingual textual annotations, shows that a consistent improvement compared to state of the art methods.||0||0|
|Annotate Wikipedia with Flickr images: Concepts and case study||Jie Xiao
|Proceedings of the 2nd International Conference on Internet Multimedia Computing and Service, ICIMCS'10||English||2010||Wikipedia, as an open editable resource, provides reliable knowledge and taxonomy. Contrast to the rich literal information, Wikipedia is lack of visual illustrations, like images and animations. Can we visually annotate Wikipedia concept and provide representative images according to its taxonomy? The huge amount of online social media, such as the tagged images in Flickr, is a good visual resource. Nevertheless, the noisy nature of the tags hinders itself. Based on the observation that images are often collected by the groups with common interest or topic, we propose a framework to visually annotate Wikipedia via social community. The contribution of our work is two-fold: (i) we diversely enrich Wikipedia with images based on its taxonomy; (ii) we introduce community effort to overcome the noisy nature of tags in harvesting images. This work shows our concept and community data collection of the proposed system. Copyright 2010 ACM.||0||0|
|On the "localness" of user-generated content||Hecht B.J.
|English||2010||The "localness" of participation in repositories of user-generated content (UGC) with geospatial components has been cited as one of UGC's greatest benefits. However, the degree of localness in major UGC repositories such as Flickr and Wikipedia has never been examined. We show that over 50 percent of Flickr users contribute local information on average, and over 45 percent of Flickr photos are local to the photographer. Across four language editions of Wikipedia, however, we find that participation is less local. We introduce the spatial content production model (SCPM) as a possible factor in the localness of UGC, and discuss other theoretical and applied implications. Copyright 2010 ACM.||0||0|
|Tag disambiguation through Flickr and Wikipedia||Anastasia Stampouli
|Improving flickr discovery through Wikipedias||Gobbo F.||CEUR Workshop Proceedings||English||2007||This paper explores how to discover unexpected information in existing folksonomies (serendipity) using extensive multilingual open source repositories as the underlying knowledge base, overcoming linguistic barriers at the same time. A web application called Flickrpedia is given as a practical example, using Flickr as the folksonomy and diverse natural language Wikipedias as the knowledge base.||0||0|