Adaptive Hausdorff Distance Based on Similarity Weighting
基于相似度加权的自适应HD算法

Hausdorff distance (HD) is a popular measure between two sets of points, and has been widely used in object matching, tracking and recognition. Based on a thorough analysis of the traditional HD and its improved variants, a new adaptive Hausdorff distance based on similarity weighting (Adaptive Hausdorff distance, AHD) is proposed. The AHD uses the number of samples which have the minimum distance to a given point in the other set as the similarity measure, and rejects those relatively large minimum distances due to their marginal influence on matching evaluation. In addition, the weighting factor is adaptively adjusted according to its minimum distance of a point to a set. Furthermore, the robustness and accuracy are well balanced by using a subset of minimum distances and weighted averaged similarity measure. Experiments show that our proposed AHD has good performance in terms of matching accuracy, and is robust to random noise and occlusion.