An Investigation into Feature Effectiveness for Multimedia Hyperlinking

The increasing amount of archival multimedia content available online is creating increasing opportunities for users who are interested in exploratory search behaviour such as browsing. The user experience with online collections could therefore be improved by enabling navigation and recommendation within multimedia archives, which can be supported by allowing a user to follow a set of hyperlinks created within or across documents. The main goal of this study is to compare the performance of different multimedia features for automatic hyperlink generation. In our work we construct multimedia hyperlinks by indexing and searching textual and visual features extracted from the blip.tv dataset. A user-driven evaluation strategy is then proposed by applying the Amazon Mechanical Turk (AMT) crowdsourcing platform, since we believe that AMT workers represent a good example of "real world" users. We conclude that textual features exhibit better performance than visual features for multimedia hyperlink construction. In general, a combination of ASR transcripts and metadata provides the best results.