Robust Similarity Measures for Mobile Object Trajectories (2002)
| Venue: | Proc. of DEXA Workshops |
| Citations: | 18 - 0 self |
BibTeX
@INPROCEEDINGS{Vlachos02robustsimilarity,
author = {Michail Vlachos and Dimitrios Gunopulos and George Kollios},
title = {Robust Similarity Measures for Mobile Object Trajectories},
booktitle = {Proc. of DEXA Workshops},
year = {2002},
pages = {721--728},
publisher = {IEEE Computer Society}
}
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Abstract
We investigate techniques for similarity analysis of spatio-temporal trajectories for mobile objects. Such kind of data may contain a great amount of outliers, which degrades the performance of Euclidean and Time Warping Distance. Therefore, here we propose the use of non-metric distance functions based on the Longest Common Subsequence (LCSS), in conjunction with a sigmoidal matching function. Finally, we compare these new methods to various L p Norms and also to Time Warping distance (for real and synthetic data) and we present experimental results that validate the accuracy and efficiency of our approach, especially under the strong presence of noise.
