Abstract
Activity videos are widespread on the Internet but current video search is limited to text tags due to limitations in recognition systems. One of the main reasons for this limitation is the wide variety of activities users could query. Thus codifying knowledge for all queries becomes problematic. Relevance Feedback (RF) is a retrieval framework that addresses this issue via interactive feedback with the user during the search session. An added benefit is that RF can also learn the subjective component of a user's search preferences. However for good retrieval performance, RF may require a large amount of user feedback for activity search. We address this issue by introducing Transfer Learning (TL) into RF. With TL, we can use auxiliary data from known classification problems different from the user's target query to decrease the needed amount of user feedback. We address key issues in integrating RF and TL and demonstrate improved performance on the challenging YouTube Action Dataset
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