Abstract
We propose the application of symmetry for texture classification. First we propose a feature vector based on the distribution of local bilateral symmetry in textured images. This feature is more effective in classifying a uniform texture versus a non-uniform texture. The feature when used with a texton-based feature improves the classification rate and is tested on 4 texture datasets. Secondly, we also present a global clustering of texture based on symmetry.
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
Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover, New York (1996)
Dana, K.J., Ginneken, B.V., Nayar, S.K., Koenderink, J.J.: Reflectance and texture of real-world surfaces. ACM Transactions on Graphics (TOG) 18(1), 1–34 (1999)
Hayman, E., Caputo, B., Fritz, M., Eklundh, J.-O.: On the Significance of Real-World Conditions for Material Classification. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 253–266. Springer, Heidelberg (2004)
Huebner, K.: A Symmetry Operator and Its Application to the RoboCup. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, pp. 274–283. Springer, Heidelberg (2004)
Keller, Y., Shkolnisky, Y.: An algebraic approach to symmetry detection. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3 (2004)
Koehler, W., Wallach, H.: Figural after-effects: an investigation of visual processes. Proc. Amer. Phil. Soc. 88, 269–357 (1944)
Kondra, S., Torre, V.: Texture classification using three circular filters. In: Sixth Indian Conference on Computer Vision, Graphics & Image Processing, ICVGIP 2008, pp. 429–434 (2008)
Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1265–1278 (2005)
Loy, G., Eklundh, J.O.: Detecting Symmetry and Symmetric Constellations of Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 508–521. Springer, Heidelberg (2006)
Manmatha, R., Sawhney, H.: Finding symmetry in intensity images. Tech. rep., University of Massachusetts Amherst MA USA (1997)
Park, H., Martin, G.R., Bhalerao, A.: An affine symmetric image model and its applications. IEEE Transactions on Image Processing 19(7), 1695–1705 (2010)
Stentiford, F.: Attention Based Facial Symmetry Detection. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 112–119. Springer, Heidelberg (2005)
Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. International Journal of Computer Vision 62(1), 61–81 (2005)
Zavidovique, B., Ges, V.D.: The s-kernel: A measure of symmetry of objects. Pattern Recognition 40(3), 839–852 (2007)
Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. In: 2006 Conference on Computer Vision and Pattern Recognition Workshop, p. 13 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kondra, S., Petrosino, A. (2012). Self-similarity and Points of Interest in Textured Images. In: Kundu, M.K., Mitra, S., Mazumdar, D., Pal, S.K. (eds) Perception and Machine Intelligence. PerMIn 2012. Lecture Notes in Computer Science, vol 7143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27387-2_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-27387-2_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27386-5
Online ISBN: 978-3-642-27387-2
eBook Packages: Computer ScienceComputer Science (R0)