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Cascaded L1-norm Minimization Learning (CLML) classifier for human detection

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4 Author(s)
Ran Xu ; Graduate University of Chinese Academy of Sciences, Beijing, China ; Baochang Zhang ; Qixiang Ye ; Jianbin Jiao

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.

Published in:

Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on

Date of Conference:

13-18 June 2010