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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiaofeng Lu Nanning Zheng Songfeng Zheng |
| Copyright Year | 2003 |
| Description | Author affiliation: Inst. of Artificial Intelligence & Robotics, Xi'an Jiaotong Univ., China (Xiaofeng Lu; Nanning Zheng; Songfeng Zheng) |
| Abstract | A very simple algorithm is used to construct an over complete set of linear sparse feature based classifiers, and AdaBoost algorithm is adopted to select part of them to form a strong classifier. During the course of feature extraction and selection, the new method minimizes the classification error directly, whereas most previous works cannot do this. An important difference between this method and other methods is that the sparse features are learned from the training set, instead of being arbitrarily defined. Experiments demonstrate that the new algorithm performs quite well. |
| Sponsorship | IEEE Signal Process. Soc |
| File Size | 291774 |
| File Format | |
| ISBN | 0780377508 |
| ISSN | 15224880 |
| DOI | 10.1109/ICIP.2003.1247388 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-09-14 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer vision Face detection Detectors Face recognition Training data Artificial intelligence Intelligent robots Feature extraction Pattern recognition Humans |
| Content Type | Text |
| Resource Type | Article |
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