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|Title:||Segregated feedback with performance-based adaptive sampling for interactive news video retrieval|
News video retrieval
|Citation:||Luan, H.-B.,Neo, S.-Y.,Goh, H.-K.,Zhang, Y.-D.,Lin, S.-X.,Chua, T.-S. (2007). Segregated feedback with performance-based adaptive sampling for interactive news video retrieval. Proceedings of the ACM International Multimedia Conference and Exhibition : 293-296. ScholarBank@NUS Repository. https://doi.org/10.1145/1291233.1291295|
|Abstract:||Existing video research incorporates the use of relevance feedback based on user-dependent interpretations to improve the retrieval results. In this paper, we segregate the process of relevance feedback into 2 distinct facets: (a) recall-directed feedback; and (b) precision-directed feedback. The recall-directed facet employs general features such as text and high level features (HLFs) to maximize efficiency and recall during feedback, making it very suitable for large corpuses. The precision-directed facet on the other hand uses many other multimodal features in an active learning environment for improved accuracy. Combined with a performance-based adaptive sampling strategy, this process continuously re-ranks a subset of instances as the user annotates. Experiments done using TRECVID 2006 dataset show that our approach is efficient and effective. Copyright 2007 ACM.|
|Source Title:||Proceedings of the ACM International Multimedia Conference and Exhibition|
|Appears in Collections:||Staff Publications|
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