Please use this identifier to cite or link to this item: https://doi.org/10.1145/1386352.1386411
Title: Adaptive multiple feedback strategies for interactive video search
Authors: Luan, H.
Zheng, Y.
Neo, S.-Y. 
Zhang, Y.
Lin, S.
Chua, T.-S. 
Keywords: Adaptive model
Interactive video search
Relevance feedback
Issue Date: 2008
Source: Luan, H.,Zheng, Y.,Neo, S.-Y.,Zhang, Y.,Lin, S.,Chua, T.-S. (2008). Adaptive multiple feedback strategies for interactive video search. CIVR 2008 - Proceedings of the International Conference on Content-based Image and Video Retrieval : 457-464. ScholarBank@NUS Repository. https://doi.org/10.1145/1386352.1386411
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. Copyright 2008 ACM.
Source Title: CIVR 2008 - Proceedings of the International Conference on Content-based Image and Video Retrieval
URI: http://scholarbank.nus.edu.sg/handle/10635/41489
ISBN: 9781605580708
DOI: 10.1145/1386352.1386411
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