Gradient Feature Selection for Online Boosting
Boosting has been widely applied in computer vision, especially after Viola and Jones's seminal work. The marriage of rectangular features and integral-image- enabled fast computation makes boosting attractive for many vision applications. However, this popular way of applying boosting normally employs an exhaustive feature selection scheme from a very large hypothesis pool, which results in a less-efficient learning process. Furthermore, this poses additional constraint on applying boosting in an onine fashion, where feature re-selection is often necessary because of varying data characteristic, but yet impractical due to the huge hypothesis pool. This paper proposes a gradient-based feature selection approach. Assuming a generally trained feature set and labeled samples are given, our approach iteratively updates each feature using the gradient descent, by minimizing the weighted least square error between the estimated feature response and the true label. In addition, we integrate the gradient-based feature selection with an online boosting framework. This new online boosting algorithm not only provides an efficient way of updating the discriminative feature set, but also presents a unified objective for both feature selection and weak classifier updating. Experiments on the person detection and tracking applications demonstrate the effectiveness of our proposal.