Item

ITEM ACTIONS

This item is discarded!DetailsSummary

Discarded

Journal Article

SVMs for Histogram Based Image Classification

MPS-Authors

/persons/resource/persons83855

Chapelle, O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Chapelle, O., Haffner, P., & Vapnik, V. (1999). SVMs for Histogram Based Image Classification. IEEE Transactions on Neural Networks, (9).

Abstract

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(mathbfx},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.