期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2014
卷号：14
期号：7
页码：50-57
出版社：International Journal of Computer Science and Network Security
摘要：Social image retrieval is important for exploiting the increasing amounts of amateur-tagged multimedia such as Flickr images. Intuitively, if different persons label similar images using the same tags, these tags are likely to reflect objective aspects of the visual content. Interpreting the relevance of a user-contributed tag with respect to the visual content of an image is an emerging problem in social image retrieval. An algorithm is proposed that scalably and reliably learns tag relevance by accumulating votes from visually similar neighbours. Treated as tag frequency, learned tag relevance is seamlessly embedded into current tag-based social image retrieval paradigms. Preliminary experiments on two thousand Flickr images demonstrate the potential of the proposed algorithm. The tag relevance learning algorithm substantially improves upon baselines for all the experiments. The results suggest that the proposed algorithm is promising for real-world applications
关键词：neighbour voting; tag relevance; user contributed tag; social image tagging
