Difference between revisions of "Exploiting Twitter and Wikipedia for the Annotation of Event Images"

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'''Exploiting Twitter and Wikipedia for the Annotation of Event Images''' - scientific work related to Wikipedia quality published in 2014, written by Philip J. McParlane and Joemon M. Jose.
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'''Exploiting Twitter and Wikipedia for the Annotation of Event Images''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Philip J. McParlane]] and [[Joemon M. Jose]].
  
 
== Overview ==
 
== Overview ==
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problem (i.e. in collection, users upload images on average 50 days after taking them, meaning "training data" is often out of date). In this paper, authors 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 experiments show and highlight the merits of exploiting social media data for annotating event images, where authors are able to achieve recommendation accuracy comparable with a state-of-the-art model.
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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problem (i.e. in collection, users upload images on average 50 days after taking them, meaning "training data" is often out of date). In this paper, authors 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 experiments show and highlight the merits of exploiting social media data for annotating event images, where authors are able to achieve recommendation accuracy comparable with a state-of-the-art model.

Revision as of 07:02, 21 September 2020

Exploiting Twitter and Wikipedia for the Annotation of Event Images - scientific work related to Wikipedia quality published in 2014, written by Philip J. McParlane and Joemon M. Jose.

Overview

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 collection, users upload images on average 50 days after taking them, meaning "training data" is often out of date). In this paper, authors 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 experiments show and highlight the merits of exploiting social media data for annotating event images, where authors are able to achieve recommendation accuracy comparable with a state-of-the-art model.