Permutation Preference Based Alternate Sampling and Clustering for Motion Segmentation

In this letter, permutation preference is used to represent the data points for the linkage clustering to segment the tracking points belonging to different motions. In order to exclude the impact of outliers, an alternate sampling and clustering strategy is performed, that iteratively alternates between sampling the hypotheses within the clusters and clustering the points with the permutation preference. As a result, points with similar permutation preferences are sampled as the hypotheses, and outliers are effectively excluded, thus, making the preferences more distinguishable and improving the clustering. The iterative interaction between sampling and clustering results in a good convergent result. The proposed method obtains robust segmentation results with the Hopkins 155 dataset, which are better than the results obtained by the state-of-the-art methods.