The Eigen-transform and applications
2006 (English)Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
2006. 70-79 p.
, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 03029743
Computer vision, Image processing, Mathematical transformations, Matrix algebra, Textures, Eigenvalues, State-of-the-art methods, Texture descriptor, Eigenvalues and eigenfunctions
IdentifiersURN: urn:nbn:se:kth:diva-156333DOI: 10.1007/11612032_8ScopusID: 2-s2.0-33744947992ISBN: 3540312196ISBN: 9783540312192OAI: oai:DiVA.org:kth-156333DiVA: diva2:766734
7th Asian Conference on Computer Vision, ACCV 2006, 13-16 January 2006, Hyderabad, India
QC 201411282014-11-282014-11-262014-11-28Bibliographically approved