This paper presents an improvement of the J-linkage algorithm for fitting multiple instances of a model to noisy data corrupted by outliers. The binary preference analysis implemented by J-linkage is replaced by a continuous (soft, or fuzzy) generalization that proves to perform better than J-linkage on simulated data, and compares favorably with state of the art methods on public domain real datasets.

T-Linkage : a continuous relaxation of J-Linkage for multi-model fitting / L. Magri, A. Fusiello - In: Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on : 23-28 June 2014[s.l] : IEEE, 2014. - ISBN 978-1-4799-5118-5. - pp. 3954-3961 (( convegno International Conference on Computer Vision and Pattern Recognition tenutosi a Columbus nel 2014 [10.1109/CVPR.2014.505].

T-Linkage : a continuous relaxation of J-Linkage for multi-model fitting

L. Magri
Primo
;
2014

Abstract

This paper presents an improvement of the J-linkage algorithm for fitting multiple instances of a model to noisy data corrupted by outliers. The binary preference analysis implemented by J-linkage is replaced by a continuous (soft, or fuzzy) generalization that proves to perform better than J-linkage on simulated data, and compares favorably with state of the art methods on public domain real datasets.
Clustering algorithms; Computational modeling; Computer vision; Data models; Estimation; Motion segmentation; Robustness
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/250901
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