document: 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 anapproximate , 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 light weight variance-based segmentation techniques , which intelligently `` relax '' the pairwise distance only in the areas that affect the least the mining operation .
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