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Related Experiment Videos

Support vector machines for histogram-based image classification.

O Chapelle1, P Haffner, V N Vapnik

  • 1Speech and Image Processing Services Research Laboratory, AT&T Labs-Research, Red Bank, NJ 07701, USA.

IEEE Transactions on Neural Networks
|February 7, 2008
PubMed
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Support vector machines (SVMs) with heavy-tailed radial basis function (RBF) kernels show improved image classification performance. A simple input remapping also enhances linear SVMs, offering a competitive alternative for high-dimensional histogram data.

Area of Science:

  • Computer Science
  • Machine Learning
  • Image Processing

Background:

  • Traditional classification methods struggle with high-dimensional feature spaces in image classification.
  • Generalization remains a challenge for existing approaches.

Purpose of the Study:

  • To evaluate the effectiveness of Support Vector Machines (SVMs) for image classification using high-dimensional histograms.
  • To investigate novel kernel functions and input transformations to improve SVM performance.

Main Methods:

  • Utilized Support Vector Machines (SVMs) with heavy-tailed Radial Basis Function (RBF) kernels of the form K(x, y) = e(-rho)Sigma(i)/xia-yia/b (a ≤ 1, b ≤ 2).
  • Tested on image classification using data from the Corel stock photo collection.
  • Compared performance against traditional polynomial and Gaussian RBF kernels.

Related Experiment Videos

  • Investigated the impact of input remapping (x(i)-->x(i)(a)) on linear SVMs.

    • Main Results:

    • Heavy-tailed RBF kernels significantly outperformed standard polynomial and Gaussian RBF kernels.
    • A simple input remapping technique notably improved the performance of linear SVMs.
    • Linear SVMs with input remapping became a viable alternative to RBF kernels for this task.

    Conclusions:

    • Heavy-tailed RBF kernels offer superior generalization for image classification with high-dimensional histogram features.
    • Input remapping presents a computationally efficient method to boost linear SVM performance.
    • SVMs, particularly with tailored kernels or input transformations, are effective for complex image classification problems.