Exploiting Twitter and Wikipedia for the annotation of event images
|Exploiting Twitter and Wikipedia for the annotation of event images|
|Author(s)||McParlane P.J., Jose J.M.|
|Published in||SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Keyword(s)||Tag recommendation, Twitter, Wikipedia (Extra: Information retrieval, Image annotation, Natural disasters, Recommendation accuracy, Social media datum, Tag recommendations, Textual content, Twitter, Wikipedia, Social networking (online))|
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Exploiting Twitter and Wikipedia for the annotation of event images is a 2014 conference paper written in English by McParlane P.J., Jose J.M. and published in SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval.
With the rise in popularity of smart phones, there has been a recent increase in the number of images taken at large social (e.g. festivals) and world (e.g. natural disasters) events which are uploaded to image sharing websites such as Flickr. As with all online images, they are often poorly annotated, resulting in a difficult retrieval scenario. To overcome this problem, many photo tag recommendation methods have been introduced, however, these methods all rely on historical Flickr data which is often problematic for a number of reasons, including the time lag problem (i.e. in our collection, users upload images on average 50 days after taking them, meaning "training data" is often out of date). In this paper, we develop an image annotation model which exploits textual content from related Twitter and Wikipedia data which aims to overcome the discussed problems. The results of our experiments show and highlight the merits of exploiting social media data for annotating event images, where we are able to achieve recommendation accuracy comparable with a state-of-the-art model. Copyright 2014 ACM.
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