Cross-media Cross-genre Information Ranking Multi-media Information Networks
Tongtao Zhang, Haibo Li, Hongzhao Huang, Heng Ji, Min-Hsuan Tsai, Shen-Fu Tsai, Thomas S. Huang · 2014
Current web technology has brought us a scenario that information about a certain topic is widely dis-persed in data from different domains and data modalities, such as texts and images from news and social media. Automatic extraction of the most informative and important multimedia summary (e.g. a ranked list of inter-connected texts and images) from massive amounts of cross-media and cross-genre data can significantly save users ’ time and effort that is consumed in browsing. In this paper, we propose a novel method to address this new task based on automatically constructed Multi-media Information Networks (MiNets) by incorporating cross-genre knowledge and inferring implicit similarity across texts and im-ages. The facts from MiNets are exploited in a novel random walk-based algorithm to iteratively propagate ranking scores across multiple data modalities. Experimental results demonstrated the effectiveness of our MiNets-based approach and the power of cross-media cross-genre inference. 1