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MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
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Proceedings of the international workshop on Workshop on multimedia information retrieval
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Proceedings of the 15th international conference on Multimedia
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Proceedings of the 15th international conference on Multimedia
Learning structured concept-segments for interactive video retrieval
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Adaptive multiple feedback strategies for interactive video search
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
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Measuring the Influence of Concept Detection on Video Retrieval
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Semantic context transfer across heterogeneous sources for domain adaptive video search
MM '09 Proceedings of the 17th ACM international conference on Multimedia
VisionGo: towards true interactivity
Proceedings of the ACM International Conference on Image and Video Retrieval
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
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IEEE Transactions on Multimedia
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IEEE Transactions on Multimedia
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IEEE Transactions on Multimedia
VisionGo: Towards video retrieval with joint exploration of human and computer
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Personalized video recommendation based on viewing history with the study on YouTube
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
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One of the main challenges in interactive concept-based video search is the insufficient relevant sample problem, especially for queries with complex semantics. To address this problem, in this paper, we propose to utilize "related samples" to learn the complex queries. The "related samples" refer to those video segments that are irrelevant to the query but relevant to some of the related concepts of the query. Different from the relevant samples which may be rare, the related samples are usually sufficient and easy to find in the search result list. Specifically, we learn a detector for the query by simultaneously leveraging the related concept detectors, as well as users' feedbacks including relevant, irrelevant, and related samples. The query detector is then employed to predict the presence of the query in new video segments. As a result, new search results can be obtained according to the query presence. Furthermore, our approach is developed based on incremental learning technique. Thus, the query detector can be efficiently updated in each feedback iteration. We conduct experiments on two real-world video datasets: TRECVID 2008 and Youtube datasets. The experimental results demonstrate the effectiveness and efficiency of the proposed approach.