Video to Article Hyperlinking by Multiple Tag Property Exploration

Abstract

Showing video and article on the same page, as done by official web agencies such as CNN.com and Yahoo!, provides a practical way for convenient information digestion. However, as the absence of article, this layout is infeasible for mainstream web video repositories like YouTube. This paper investigates the problem of hyperlinking web videos to relevant articles available on the Web. Given a video, the task is accomplished by firstly identifying its contextual tags (e.g., who are doing what at where and when) and then employing a search based association to relevant articles. Specifically, we propose a multiple tag property exploration (mTagPE) approach to identify contextual tags, where tag relevance, tag clarity and tag correlation are defined and measured by leveraging visual duplicate analyses, online knowledge bases and tag co-occurrence. Then, the identification task is formulated as a random walk along a tag relation graph that smoothly integrates the three properties. The random walk aims at picking up relevant, clear and correlated tags as a set of contextual tags, which is further treated as a query to issue commercial search engines to obtain relevant articles. We have conducted experiments on a largescale web video dataset. Both objective performance evaluations and subjective user studies show the effectiveness of the proposed hyperlinking. It produces more accurate contextual tags and thus a larger number of relevant articles than other approaches.