A novel system for the semi automatic annotation of event images
|A novel system for the semi automatic 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)||Photo tag recommendation, Twitter, Wikipedia (Extra: Information retrieval, Photography, Large scale events, Recent researches, Semi-automatic annotation, Semi-automatics, Tag recommendations, Twitter, Visual appearance, Wikipedia, Research)|
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A novel system for the semi automatic 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, taking and sharing photographs has never been more openly accessible. Further, photo sharing websites, such as Flickr, have made the distribution of photographs easy, resulting in an increase of visual content uploaded online. Due to the laborious nature of annotating images, however, a large percentage of these images are unannotated making their organisation and retrieval difficult. Therefore, there has been a recent research focus on the automatic and semi-automatic process of annotating these images. Despite the progress made in this field, however, annotating images automatically based on their visual appearance often results in unsatisfactory suggestions and as a result these models have not been adopted in photo sharing websites. Many methods have therefore looked to exploit new sources of evidence for annotation purposes, such as image context for example. In this demonstration, we instead explore the scenario of annotating images taken at a large scale events where evidences can be extracted from a wealth of online textual resources. Specifically, we present a novel tag recommendation system for images taken at a popular music festival which allows the user to select relevant tags from related Tweets and Wikipedia content, thus reducing the workload involved in the annotation process. Copyright 2014 ACM.
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