Adaptive multiple feedback strategies for interactive video search

Authors:
Huanbo Luan;Yantao Zheng;Shi-Yong Neo;Yongdong Zhang;Shouxun Lin;Tat-Seng Chua
Affiliations:
Chinese Academy of Sciences, Beijing, China;National University of Singapore, Singapore, Singapore;National University of Singapore, Singapore, Singapore;Chinese Academy of Sciences, Beijing, China;Chinese Academy of Sciences, Beijing, China;National University of Singapore, Singapore, Singapore
Venue:
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Year:
2008

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Abstract

In this paper, we propose adaptive multiple feedback strategies for interactive video retrieval. We first segregate interactive feedback into 3 distinct types (recall-driven relevance feedback, precision-driven active learning and locality-driven relevance feedback) so that a generic interaction mechanism with more flexibility can be performed to cover different search queries and different video corpuses. Our system facilitates expert searchers to flexibly decide on the types of feedback they want to employ under different situations. To cater to the large number of novice users (non-expert users), an adaptive option is built-in to learn the expert user behavior so as to provide recommendations on the next feedback strategy, leading to a more precise and personalized search for the novice users. Experimental results on TRECVID news video corpus demonstrate that our proposed adaptive multiple feedback strategies are effective.