Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 North Mathews Avenue, Urbana, IL 61801, USA.
IEEE Trans Pattern Anal Mach Intell. 2012 May;34(5):850-62. doi: 10.1109/TPAMI.2011.191.
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.
社交媒体网络包含内容和上下文特定的信息。大多数现有的方法要么使用这两种信息中的一种来进行多媒体挖掘和检索。实际上，内容和上下文信息都是挖掘的丰富信息源，只有结合使用这两者，才能充分发挥挖掘和处理算法的威力。本文提出了一种新的算法，该算法可以挖掘社交媒体网络中的上下文和内容链接，以发现潜在的语义空间。这种将多媒体对象映射到潜在特征向量的方法可以使用任何现成的多媒体检索算法。与多媒体分析中的最新潜在方法相比，该算法通过挖掘多媒体对象之间内容链接的几何结构，有效地解决了稀疏上下文链接的问题。具体来说，对于多媒体注释，我们展示了一种有效的算法，该算法可以通过同时利用上下文和内容信息，根据相关语义概念之间的潜在结构，直接构建注释模型。我们在包含用户标签和图像链接的 Flickr 数据集上进行了实验，结果表明，我们的方法优于最新的多媒体检索技术。
