The eigen-transform and applications

  • Authors:

  • Alireza Tavakoli Targhi;Eric Hayman;Jan-Olof Eklundh;Mehrdad Shahshahani

  • Affiliations:

  • Computational Vision and Active Perception Laboratory, School of Computer Science and Communication, Royal Institute of Technology (KTH), Stockholm, Sweden;Computational Vision and Active Perception Laboratory, School of Computer Science and Communication, Royal Institute of Technology (KTH), Stockholm, Sweden;Computational Vision and Active Perception Laboratory, School of Computer Science and Communication, Royal Institute of Technology (KTH), Stockholm, Sweden;Institute for Studies in Theoretical Physics and Mathematics (IPM), Tehran, Iran

  • Venue:
  • ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
  • Year:
  • 2006

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Abstract

This paper introduces a novel texture descriptor, the Eigen-transform. The transform provides a measure of roughness by considering the eigenvalues of a matrix which is formed very simply by inserting the greyvalues of a square patch around a pixel directly into a matrix of the same size. The eigenvalue of largest magnitude turns out to give a smoothed version of the original image, but the eigenvalues of smaller magnitude encode high frequency information characteristic of natural textures. A major advantage of the Eigen-transform is that it does not fire on straight, or locally straight, brightness edges, instead it reacts almost entirely to the texture itself. This is in contrast to many other descriptors such as Gabor filters or the standard deviation of greyvalues of the patch. These properties make it remarkably well suited to practical applications. Our experiments focus on two main areas. The first is in bottom-up visual attention where textured objects pop out from the background using the Eigen-transform. The second is unsupervised texture segmentation with particular emphasis on real-world, cluttered indoor environments. We compare results with other state-of-the-art methods and find that the Eigen-transform is highly competitive, despite its simplicity and low dimensionality.