Support vector machines 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 dimensio...

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Bibliographic Details
Published inIEEE transactions on neural networks Vol. 10; no. 5; pp. 1055 - 1064
Main Authors Chapelle, O., Haffner, P., Vapnik, V.N.
Format Journal Article
LanguageEnglish
Published United States IEEE 1999
Subjects
Classification
Classification tree analysis
Histograms
Image classification
Image databases
Image recognition
Kernel
Kernels
Neural networks
Polynomials
Support vector machine classification
Support vector machines
Tasks
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Summary: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(x, y)=e/sup -/spl rho///spl Sigma//sub i//sup |xia-yia|b/ with a /spl les/1 and b/spl les/2 are evaluated on the classification of images extracted from the Corel stock photo collection and shown to far outperform traditional polynomial or Gaussian radial basis function (RBF) kernels. Moreover, we observed that a simple remapping of the input x/sub i//spl rarr/x/sub i//sup a/ improves the performance of linear SVM to such an extend that it makes them, for this problem, a valid alternative to RBF kernels.
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ISSN:1045-9227
1941-0093
DOI:10.1109/72.788646