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Mean shift, mode seeking, and clustering
Abstract
Mean shift, a simple interactive procedure that shifts each data point to the average of data points in its neighborhood is generalized and analyzed in the paper. This generalization makes some k-means like clustering algorithms its special cases. It is shown that mean shift is a mode-seeking process on the surface constructed with a "shadow" kernal. For Gaussian kernels, mean shift is a gradient mapping. Convergence is studied for mean shift iterations. Cluster analysis if treated as a deterministic problem of finding a fixed point of mean shift that characterizes the data. Applications in clustering and Hough transform are demonstrated. Mean shift is also considered as an evolutionary strategy that performs multistart global optimization.>
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Stochastic global optimization methods part I: Clustering methods
Rinnooy Kan, A. H. G., & Timmer, G. T. (1987) · Mathematical Programming · doi:10.1007/bf02592070 · Cited by 463
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Metadata
- Authors
- Cheng, Y.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1995
- Volume
- 17
- Issue
- 8
- Pages
- 790-799
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2022686119
- Cited by
- 3,856
- References
- 1
- FWCI
- 3.35
- Type
- journal article
- Language
- en
- Open access
- closed
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- Cheng, Y. (1995). Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.400568
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
