A new approach to retrieve video by example video clip
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A survey on the use of relevance feedback for information access systems
The Knowledge Engineering Review
International Journal of Computer Vision
Extreme video retrieval: joint maximization of human and computer performance
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
An integrated approach to video retrieval
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
Adaptive multiple feedback strategies for interactive video search
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
A framework for classifier adaptation and its applications in concept detection
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Using large-scale web data to facilitate textual query based retrieval of consumer photos
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Can social tagged images aid concept-based video search?
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
IEEE Transactions on Knowledge and Data Engineering
Bayesian filter based behavior recognition in workflows allowing for user feedback
Computer Vision and Image Understanding
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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