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Cascaded L1-norm Minimization Learning (CLML) Classifier for Human Detection

9 years 11 months ago
Cascaded L1-norm Minimization Learning (CLML) Classifier for Human Detection
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.
Ran Xu, Baochang Zhang, Qixiang Ye, jian bin Jiao
Added 03 Jul 2010
Updated 03 Jul 2010
Type Conference
Year 2010
Where CVPR
Authors Ran Xu, Baochang Zhang, Qixiang Ye, jian bin Jiao
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