Max-Margin Regularization for Reducing Accidentalness in Chamfer Matching

Abstract

Standard chamfer matching techniques and their state-ofthe- art extensions are utilizing object contours which only measure the mere sum of location and orientation differences of contour pixels. In our approach we are increasing the specificity of the model contour by learning the relative importance of all model points instead of treating them as independent. However, chamfer matching is still prone to accidental matches in dense clutter. To detect such accidental matches we learn the co-occurrence of generic background contours to further eliminate the number of false detections. Since, clutter only interferes with the foreground model contour we learn where to place the background contours with respect to the foreground object boundary. The co-occurrence of foreground model points and background contours are both integrated into a single max-margin framework. Thus our approach combines the advantages of accurately detecting objects or parts via chamfer matching and the robustness of a max-margin learning. Our results on standard benchmark datasets show that our method significantly outperforms current directional chamfer matching, thus redefining the state-of-the-art in this field.