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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aase, S.O., Husøy, J.H., Waldemar, P.: A critique of SVD-based image coding systems. In: Proc. ISCAS, vol. 4, pp. 13–16 (1999)
Brodatz, P.: Textures. Dover (1966)
Comaniciu, D., Meer, P.: Mean shift analysis and applications. In: Proc. ICCV, pp. 1197–1203 (1999)
Cootes, T., Edwards, G., Taylor, C.: Active appearance models. PAMI 23(6), 681–685 (2001)
de Ridder, D., Kittler, J., Lemmers, O., Duin, R.: The adaptive subspace map for texture segmentation. In: Proc. ICPR (2000)
Deng, Y., Manjunath, B.: JSEG (1999), http://vision.ece.ucsb.edu/segmentation/jseg/software/
Deng, Y., Manjunath, B.: Unsupervised segmentation of color-texture regions in images and video. PAMI 23(8), 800–810 (2001)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. PAMI 20(11), 1254–1259 (1998)
Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. IJCV 43(1), 29–44 (2001)
Mallikarjuna, P., Fritz, M., Tavakoli Targhi, A., Hayman, E., Caputo, B., Eklundh, J.-O.: The KTH-TIPS2 databases, http://www.nada.kth.se/cvap/databases/kth-tips
Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. IJCV 43(1), 7–27 (2001)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. ICCV, July 2001, vol. 2, pp. 416–423 (July 2001)
Murase, H., Nayar, S.: Visual learning and recognition of 3D objects from appearance. IJCV 14(1) (1995)
Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllonen, J., Huovinen, S.: Outex: New framework for empirical evaluation of texture analysis algorithms. In: Proc. ICPR, pp. I: 701–706 (2002)
Ojala, T., Pietikainen, M.: Unsupervised texture segmentation using feature distributions. Pattern Recognition 32(3), 477–486 (1999)
Tavakoli Targhi, A., Shademan, A.: Clustering of Singular Value Decomposition of Image Data with Applications to Texture Classification. In: Proc. SPIE, July 2003, vol. 5150, pp. 972–979 (2003)
Pietikainen, M., Nurmela, T., Maenpaa, T., Turtinen, M.: View-based recognition of real-world textures. Pattern Recognition 37(2), 313–323 (2004)
Ramström, O., Christensen, H.: Object detection using background context. In: Proc. ICPR, pp. III: 45–48 (2004)
Schiele, B., Crowley, J.: Recognition without correspondence using multidimensional receptive field histograms. IJCV 36(1), 31–50 (2000)
Sharon, E., Brandt, A., Basri, R.: Segmentation and Boundary Detection Using Multiscale Intensity Measurements. In: Proc. CVPR, pp. I: 469–476 (2001)
Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI 22(8) (August 2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Targhi, A.T., Hayman, E., Eklundh, JO., Shahshahani, M. (2006). The Eigen-Transform and Applications. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_8
Download citation
DOI: https://doi.org/10.1007/11612032_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31219-2
Online ISBN: 978-3-540-32433-1
eBook Packages: Computer ScienceComputer Science (R0)