Kernel Codebooks for Scene Categorization

@inproceedings{Gemert2008KernelCF,
  title={Kernel Codebooks for Scene Categorization},
  author={Jan C. van Gemert and Jan-Mark Geusebroek and Cor J. Veenman and Arnold W. M. Smeulders},
  booktitle={ECCV},
  year={2008}
}
This paper introduces a method for scene categorization by modeling ambiguity in the popular codebook approach. The codebook approach describes an image as a bag of discrete visual codewords, where the frequency distributions of these words are used for image categorization. There are two drawbacks to the traditional codebook model: codeword uncertainty and codeword plausibility. Both of these drawbacks stem from the hard assignment of visual features to a single codeword. We show that allowing… CONTINUE READING
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References

Publications referenced by this paper.
SHOWING 1-10 OF 26 REFERENCES

Creating efficient codebooks for visual recognition

  • Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
  • 2005
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

  • 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)
  • 2006
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

A Bayesian hierarchical model for learning natural scene categories

  • 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
  • 2005
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Scene Classification Using a Hybrid Generative/Discriminative Approach

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2008
VIEW 1 EXCERPT

Accurate Object Localization with Shape Masks

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition
  • 2007
VIEW 1 EXCERPT

Image Classification using Random Forests and Ferns

  • 2007 IEEE 11th International Conference on Computer Vision
  • 2007
VIEW 1 EXCERPT

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