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SVMs for Histogram Based Image Classification




Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that Support Vector Machines (SVM) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form $K(mathbf{x},mathbf{y})=e^{-rhosum_i |x_i^a-y_i^a|^{b}}$ with $aleq 1$ and $b leq 2$ are evaluated on the classification of images extracted from the Corel Stock Photo Collection and shown to far outperform traditional polynomial or Gaussian RBF kernels. Moreover, we observed that a simple remapping of the input $x_i rightarrow x_i^a$ improves the performance of linear SVMs to such an extend that it makes them, for this problem, a valid alternative to RBF kernels.

Author(s): Chapelle, O. and Haffner, P. and Vapnik, V.
Journal: IEEE Transactions on Neural Networks
Number (issue): 9
Year: 1999
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: GZIP


  title = {SVMs for Histogram Based Image Classification},
  author = {Chapelle, O. and Haffner, P. and Vapnik, V.},
  journal = {IEEE Transactions on Neural Networks},
  number = {9},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {1999}