Robust object detection scheme using feature selection
Feature selection is an important issue for object detection. In this paper, we propose an effective wrapper-based feature selection scheme using Binary Particle Swarm Optimization (BPSO) and Support Vector Machine (SVM) for object detection. In our algorithm, Scale-Invariant Feature Transform (SIFT) descriptors in a patch around the keypoints are extracted as the initial feature representations. The initial feature set is fed into the feature selection module in which the BPSO searches the feature space, and a SVM classifier serves as an evaluator for the performance of the feature subset selected by the BPSO. We tested the proposed detection scheme on the UIUC car dataset and our results show that feature selection scheme not only improves the detection accuracy but also enhances the detection efficiency.