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SVMs for Histogram Based Image Classification

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83855

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

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Zitation

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


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-E73D-5
Zusammenfassung
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.