Object classification by fusing SVMs and Gaussian mixtures

@article{Deselaers2010ObjectCB,
  title={Object classification by fusing SVMs and Gaussian mixtures},
  author={Thomas Deselaers and Georg Heigold and Hermann Ney},
  journal={Pattern Recognition},
  year={2010},
  volume={43},
  pages={2476-2484}
}
We present a new technique that employs support vector machines (SVMs) and Gaussian mixture densities (GMDs) to create a generative/discriminative object classification technique using local image features. In the past, several approaches to fuse the advantages of generative and discriminative approaches were presented, often leading to improved robustness and recognition accuracy. Support vector machines are a well known discriminative classification framework but, similar to other… CONTINUE READING
Highly Cited
This paper has 55 citations. REVIEW CITATIONS
26 Citations
34 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 26 extracted citations

55 Citations

01020'11'13'15'17
Citations per Year
Semantic Scholar estimates that this publication has 55 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 34 references

The PASCAL Visual Object Classes Challenge 2006 (VOC2006) Results

  • M. Everingham, A. Zisserman, C.K.I. Williams, L. Van Gool
  • Technical report, PASCAL Network of Excellence,
  • 2006
Highly Influential
3 Excerpts

Similar Papers

Loading similar papers…