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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ran Xu Baochang Zhang Qixiang Ye Jianbin Jiao |
| Copyright Year | 2010 |
| Description | Author affiliation: Graduate University of Chinese Academy of Sciences, Beijing, China (Ran Xu; Qixiang Ye; Jianbin Jiao) || School of Automation Science and Electrical Engineering, Beihang University, Beijing, China (Baochang Zhang) |
| Abstract | This paper proposes a new learning method, which integrates feature selection with classifier construction for human detection via solving three optimization models. Firstly, the method trains a series of weak-classifiers by the proposed L1-norm Minimization Learning (LML) and min-max penalty function models. Secondly, the proposed method selects the weak-classifiers by using the integer optimization model to construct a strong classifier. The L1-norm minimization and integer optimization models aim to find the minimal VC-dimension for weak and strong classifiers respectively. Finally, the method constructs a cascade of LML (CLML) classifier to reach higher detection rates and efficiency. Histograms of Oriented Gradients features of variable-size blocks (v-HOG) are employed as human representation to verify the proposed method. Experiments conducted on INRIA human test set show more superior detection rates and speed than state-of-the-art methods. |
| Starting Page | 89 |
| Ending Page | 96 |
| File Size | 507972 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469840 |
| ISSN | 10636919 |
| e-ISBN | 9781424469857 |
| DOI | 10.1109/CVPR.2010.5540224 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Humans Support vector machines Support vector machine classification Histograms Minimization methods Kernel Optimization methods Testing Radio access networks Automation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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