Exploring context and content links in social media: A latent space method

Guo Jun Qi, Charu Aggarwal, Qi Tian, Heng Ji, Thomas S. Huang

Research output: Contribution to journalArticlepeer-review

Abstract

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.

Original languageEnglish (US)
Article number6035718
Pages (from-to)850-862
Number of pages13
JournalIEEE transactions on pattern analysis and machine intelligence
Volume34
Issue number5
DOIs
StatePublished - 2012
Externally publishedYes

Keywords

  • Context and content links
  • latent semantic space
  • low-rank method
  • multimedia information networks
  • social Media

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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