Robust Object Detection using Fast Feature Selection from Huge Feature Sets

This paper describes an efficient feature selection method that quickly selects a small subset out of a given huge feature set; for building robust object detection systems. In this filter-based method, features are selected not only to maximize their relevance with the target class but also to minimize their mutual dependency. As a result, the selected feature set contains only highly informative and non-redundant features, which significantly improve classification performance when combined. The relevance and mutual dependency of features are measured by using conditional mutual information (CMI) in which features and classes are treated as discrete random variables. Experiments on different huge feature sets have shown that the proposed CMI-based feature selection can both reduce the training time significantly and achieve high accuracy.