Accelerated Mean Shift For Static And Streaming Environments

Mean Shift is a well-known clustering algorithm that has attractive properties such as the ability to find non convex and local clusters even in high dimensional spaces, while remaining relatively insensitive to outliers. However, due to its poor computational performance, real-world applications are limited. In this article, we propose a novel acceleration strategy for the traditional Mean Shift algorithm, along with a two-layer strategy, resulting in a considerable performance increase, while maintaining high cluster quality. We also show how to to find clusters in a streaming environment with bounded memory, in which queries need to be answered at interactive rates, and for which no mean shift-based algorithm currently exists. Our online structure is updated at very minimal cost and as infrequently as possible, and we show how to detect the time at which an update needs to be triggered. Our technique is validated extensively in both static and streaming environments.

@InProceedings { VTE15,
  author       = "Ende, Daniel van der and Thiery, Jean-Marc and Eisemann, Elmar",
  title        = "Accelerated Mean Shift For Static And Streaming Environments",
  booktitle    = "DATA ANALYTICS 2015, The Fourth International Conference on Data Analytics",
  volume       = "4",
  pages        = "140--145",
  month        = "July",
  year         = "2015",
  keywords     = "mean shift, clustering",
  url          = "http://graphics.tudelft.nl/Publications-new/2015/VTE15"
}