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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Loy, C.C. Hospedales, T.M. Tao Xiang Shaogang Gong |
| Copyright Year | 2012 |
| Description | Author affiliation: School of EECS, Queen Mary University of London, UK (Hospedales, T.M.; Tao Xiang; Shaogang Gong) || Vision Semantics Ltd, UK (Loy, C.C.) |
| Abstract | Learning from streams of evolving and unbounded data is an important problem, for example in visual surveillance or internet scale data. For such large and evolving real-world data, exhaustive supervision is impractical, particularly so when the full space of classes is not known in advance therefore joint class discovery (exploration) and boundary learning (exploitation) becomes critical. Active learning has shown promise in jointly optimising exploration-exploitation with minimal human supervision. However, existing active learning methods either rely on heuristic multi-criteria weighting or are limited to batch processing. In this paper, we present a new unified framework for joint exploration-exploitation active learning in streams without any heuristic weighting. Extensive evaluation on classification of various image and surveillance video datasets demonstrates the superiority of our framework over existing methods. |
| Starting Page | 1560 |
| Ending Page | 1567 |
| File Size | 740297 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467312264 |
| ISSN | 10636919 |
| e-ISBN | 9781467312288 |
| e-ISBN | 9781467312271 |
| DOI | 10.1109/CVPR.2012.6247847 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-16 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Labeling Joints Streaming media Humans Uncertainty Bayesian methods Sun |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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