Histogram intersection kernel for image classification

@article{Barla2003HistogramIK,
  title={Histogram intersection kernel for image classification},
  author={Annalisa Barla and Francesca Odone and Alessandro Verri},
  journal={Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429)},
  year={2003},
  volume={3},
  pages={III-513}
}
In this paper we address the problem of classifying images, by exploiting global features that describe color and illumination properties, and by using the statistical learning paradigm. The contribution of this paper is twofold. First, we show that histogram intersection has the required mathematical properties to be used as a kernel function for support vector machines (SVMs). Second, we give two examples of how a SVM, equipped with such a kernel, can achieve very promising results on image… CONTINUE READING

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  • The results of these experiments were promising: we obtained, for various bins’ choices, recognition rates around 90% for a RGB representation and rates above 92% for a HSV representation.

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