Incremental learning with Gaussian mixture models

@inproceedings{Kristan2008IncrementalLW,
  title={Incremental learning with Gaussian mixture models},
  author={Matej Kristan and Danijel Skocaj and Alex0161 Leonardis},
  year={2008}
}
In this paper we propose a new incremental estimation of Gaussian mixture models which can be used for applications of online learning. Our approach allows for adding new samples incrementally as well as removing parts of the mixture by the process of unlearning. Low complexity of the mixtures is maintained through a novel compression algorithm. In contrast to the existing approaches, our approach does not require fine-tuning parameters for a specific application, we do not assume specific… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.
11 Citations
18 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 11 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 18 references

Leonardis . Continuous learning of simple visual concepts using Incremental Kernel Density Estimation

  • M. Song, H. Wang
  • International Conference on Computer Vision…
  • 2008

Time-evolving adaptive mixtures

  • W. F. Szewczyk
  • Technical report, National Security Agency
  • 2005
2 Excerpts

Sequential density approximation through mode propagation: Applications to background modeling

  • B. Han, D. Comaniciu, L. Davis
  • Asian Conf. Computer Vision
  • 2004
1 Excerpt

Similar Papers

Loading similar papers…