Geo-location driven image tagging via cross-domain learning

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• 作者：Weizhi Nie ; Anan Liu ; Zhongyang Wang ; Yuting Su
• 刊名：Multimedia Systems
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：22
• 期：4
• 页码：395-404
• 全文大小：1,324 KB
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Operating Systems
  Data Storage Representation
  Data Encryption
  Computer Graphics
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1432-1882
• 卷排序：22

文摘

With the rapid development of location-based social network, more and more multimedia data are uploaded by users. These data always include large-scale of independent information with both textual and visual contents. To bridge the semantic gap in between, we propose a novel cross-domain learning method for automatic image annotation with geo-location information. First, we propose the topic model-based method for popular concept extraction to adaptively construct cross-domain datasets. Then these concepts are utilized to collect the visual correlation information from Flickr. Finally, we leverage cross-domain learning method for model learning. The comparison experiments on cross-domain datasets are conducted to demonstrate the superiority of the proposed method.KeywordsLocation-based social networkImage annotationCross-domain dataMachinelearningSocial media