Please use this identifier to cite or link to this item: https://doi.org/10.1145/1459359.1459393
Title: Exploring knowledge of sub-domain in a multi-resolution bootstrapping framework for concept detection in news
Authors: Wang, G.
Chua, T.-S. 
Zhao, M.
Keywords: Bootstrapping.
Domain knowledge
Multiresolution analysis
Text semantics
Transductive learning
Unlabeled data
Issue Date: 2008
Source: Wang, G., Chua, T.-S., Zhao, M. (2008). Exploring knowledge of sub-domain in a multi-resolution bootstrapping framework for concept detection in news. MM'08 - Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium and Workshops : 249-258. ScholarBank@NUS Repository. https://doi.org/10.1145/1459359.1459393
Abstract: In this paper, we present a model based on a multi-resolution, multi-source and multi-modal (M3) bootstrapping framework that exploits knowledge of sub-domains for concept detection in news video. Because the characteristics and distributions of data in different sub-domains are different, we model and analyze the video in each sub-domain separately using a transductive framework. Along with this framework, we propose a "pseudo-Vapnik combined error bound" to tackle the problem of imbalanced distribution of training data in certain segments of sub-domains. For effective fusion of multi-modal features, we utilize multi-resolution inference and constraints to permit evidences from different modal features to support each other. Finally, we employ a bootstrapping technique to leverage unlabeled data to boost the overall system performance. We test our framework by detecting semantic concepts in the TRECVID 2004 dataset. Experimental results demonstrate that our approach is effective. Copyright 2008 ACM.
Source Title: MM'08 - Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium and Workshops
URI: http://scholarbank.nus.edu.sg/handle/10635/41147
ISBN: 9781605583037
DOI: 10.1145/1459359.1459393
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