Abstract To solve the problem of feature misalignment appearing in object detection, a multi-configuration feature bag is put forward and used to describe variant misalignments for the same feature. By boosting algorithm, the most discriminative feature bags, which consist of single features and their corresponding misalignment cases, are selected in the phase of classifier training. Based on those best feature bags, weak classifiers are generated and combined into the final ensemble classifier. Moreover, multiple instance learning is introduced to efficiently evaluate the discriminative ability of feature bags and train feature bag classifiers. The experimental results on public face dataset demonstrate that the proposed algorithm is more robust than the traditional method when the problem of feature misalignment is considered. Furthermore, compared to the feature bag with fixed size, the proposed multi-configuration feature bag models the feature misalignment better, gets smaller detector size while improving the detection accuracy.
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2011, Vol. 24 
