Exploring Context and Content Links in Social Media: A Latent Space Method

—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-theshelf 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 b...

• Type: Journal
• Year: 2012
• Where: PAMI
• Authors: Guo-Jun Qi, Charu C. Aggarwal, Qi Tian, Heng Ji, Thomas S. Huang
• Keywords: Feature Vectors, Latent Structure, Multimedia Analysis, PAMI 2012, Software Engineering