Global distance-based segmentation of trajectories

This work introduces distance-based criteria for segmentation of object trajectories. Segmentation leads to simplification of the original objects into smaller, less complex primitives that are better suited for storage and retrieval purposes. Previous work on trajectory segmentation attacked the problem locally, segmenting separately each trajectory of the database. Therefore, they did not directly optimize the inter-object separability, which is necessary for mining operations such as searching, clustering, and classification on large databases. In this paper we analyze the trajectory segmentation problem from a global perspective, utilizing data aware distance-based optimization techniques, which optimize pairwise distance estimates hence leading to more efficient object pruning. We first derive exact solutions of the distance-based formulation. Due to the intractable complexity of the exact solution, we present an approximate, greedy solution that exploits forward searching of locally optimal solutions. Since the greedy solution also imposes a prohibitive computational cost, we also put forward more lightweight variance-based segmentation techniques, which intelligently “relax ” the pairwise distance only in the areas that affect the least the mining operations.