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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhongwen Xu Tsang, I.W. Yi Yang Zhigang Ma Hauptmann, A.G. |
| Copyright Year | 2014 |
| Description | Author affiliation: ITEE, Univ. of Queensland, Brisbane, QLD, Australia (Zhongwen Xu; Yi Yang) || QCIS, Univ. of Technol., Sydney, NSW, Australia (Tsang, I.W.) || Sch. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA (Zhigang Ma; Hauptmann, A.G.) |
| Abstract | We address the challenging problem of utilizing related exemplars for complex event detection while multiple features are available. Related exemplars share certain positive elements of the event, but have no uniform pattern due to the huge variance of relevance levels among different related exemplars. None of the existing multiple feature fusion methods can deal with the related exemplars. In this paper, we propose an algorithm which adaptively utilizes the related exemplars by cross-feature learning. Ordinal labels are used to represent the multiple relevance levels of the related videos. Label candidates of related exemplars are generated by exploring the possible relevance levels of each related exemplar via a cross-feature voting strategy. Maximum margin criterion is then applied in our framework to discriminate the positive and negative exemplars, as well as the related exemplars from different relevance levels. We test our algorithm using the large scale TRECVID 2011 dataset and it gains promising performance. |
| Starting Page | 97 |
| Ending Page | 104 |
| File Size | 587866 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479951185 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2014.20 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-06-23 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Videos Event detection Feature extraction Kernel Tires Prediction algorithms Vehicles |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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