Similarity of Polygonal Curves in the Presence of Outliers
Jean-Lou De Carufel, Amin Gheibi, Anil Maheshwari, J\"org-R\"udiger, Sack, Christian Scheffer

TL;DR
This paper introduces a robust variant of the Fréchet distance for polygonal curves that accounts for outliers by minimizing ignored subcurve lengths or maximizing preserved subcurve lengths, providing approximation algorithms with provable bounds.
Contribution
It formulates the MinEx and MaxIn problems as robust measures of curve similarity, proves their algebraic complexity, and offers an efficient approximation algorithm with explicit error bounds.
Findings
Exact solutions are algebraically intractable.
Proposed algorithm approximates solutions within additive error.
Algorithm runs in near-cubic time relative to input size.
Abstract
The Fr\'{e}chet distance is a well studied and commonly used measure to capture the similarity of polygonal curves. Unfortunately, it exhibits a high sensitivity to the presence of outliers. Since the presence of outliers is a frequently occurring phenomenon in practice, a robust variant of Fr\'{e}chet distance is required which absorbs outliers. We study such a variant here. In this modified variant, our objective is to minimize the length of subcurves of two polygonal curves that need to be ignored (MinEx problem), or alternately, maximize the length of subcurves that are preserved (MaxIn problem), to achieve a given Fr\'{e}chet distance. An exact solution to one problem would imply an exact solution to the other problem. However, we show that these problems are not solvable by radicals over and that the degree of the polynomial equations involved is unbounded in general.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComputational Geometry and Mesh Generation · Advanced Numerical Analysis Techniques · Image and Object Detection Techniques
