Fast Mean Shift with Accurate and Stable Convergence

Ping Wang, Dongryeol Lee, Alexander Gray, James M. Rehg
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:604-611, 2007.

Abstract

Mean shift is a powerful but computationally expensive method for nonparametric clustering and optimization. It iteratively moves each data point to its local mean until convergence. We introduce a fast algorithm for computing mean shift based on the dual-tree. Unlike previous speed-up attempts, our algorithm maintains a relative error bound at each iteration, resulting in significantly more stable and accurate convergence. We demonstrate the benefit of our method in clustering experiments with real and synthetic data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v2-wang07d, title = {Fast Mean Shift with Accurate and Stable Convergence}, author = {Wang, Ping and Lee, Dongryeol and Gray, Alexander and Rehg, James M.}, booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics}, pages = {604--611}, year = {2007}, editor = {Meila, Marina and Shen, Xiaotong}, volume = {2}, series = {Proceedings of Machine Learning Research}, address = {San Juan, Puerto Rico}, month = {21--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v2/wang07d/wang07d.pdf}, url = {https://proceedings.mlr.press/v2/wang07d.html}, abstract = {Mean shift is a powerful but computationally expensive method for nonparametric clustering and optimization. It iteratively moves each data point to its local mean until convergence. We introduce a fast algorithm for computing mean shift based on the dual-tree. Unlike previous speed-up attempts, our algorithm maintains a relative error bound at each iteration, resulting in significantly more stable and accurate convergence. We demonstrate the benefit of our method in clustering experiments with real and synthetic data.} }
Endnote
%0 Conference Paper %T Fast Mean Shift with Accurate and Stable Convergence %A Ping Wang %A Dongryeol Lee %A Alexander Gray %A James M. Rehg %B Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2007 %E Marina Meila %E Xiaotong Shen %F pmlr-v2-wang07d %I PMLR %P 604--611 %U https://proceedings.mlr.press/v2/wang07d.html %V 2 %X Mean shift is a powerful but computationally expensive method for nonparametric clustering and optimization. It iteratively moves each data point to its local mean until convergence. We introduce a fast algorithm for computing mean shift based on the dual-tree. Unlike previous speed-up attempts, our algorithm maintains a relative error bound at each iteration, resulting in significantly more stable and accurate convergence. We demonstrate the benefit of our method in clustering experiments with real and synthetic data.
RIS
TY - CPAPER TI - Fast Mean Shift with Accurate and Stable Convergence AU - Ping Wang AU - Dongryeol Lee AU - Alexander Gray AU - James M. Rehg BT - Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics DA - 2007/03/11 ED - Marina Meila ED - Xiaotong Shen ID - pmlr-v2-wang07d PB - PMLR DP - Proceedings of Machine Learning Research VL - 2 SP - 604 EP - 611 L1 - http://proceedings.mlr.press/v2/wang07d/wang07d.pdf UR - https://proceedings.mlr.press/v2/wang07d.html AB - Mean shift is a powerful but computationally expensive method for nonparametric clustering and optimization. It iteratively moves each data point to its local mean until convergence. We introduce a fast algorithm for computing mean shift based on the dual-tree. Unlike previous speed-up attempts, our algorithm maintains a relative error bound at each iteration, resulting in significantly more stable and accurate convergence. We demonstrate the benefit of our method in clustering experiments with real and synthetic data. ER -
APA
Wang, P., Lee, D., Gray, A. & Rehg, J.M.. (2007). Fast Mean Shift with Accurate and Stable Convergence. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 2:604-611 Available from https://proceedings.mlr.press/v2/wang07d.html.

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