Abstract
We have developed a new framework for scale invariant texture analysis using multi-scale local autocorrelation features. The multi-scale features are made of concatenated feature vectors of different scales, which are calculated from higher-order local autocorrelation functions. To classify different types of textures among the given test images, a linear discriminant classifier (LDA) is employed in the multi-scale feature space. The scale rate of test patterns in their reduced subspace can also be estimated by principal component analysis (PCA). This subspace represents the scale variation of each scale step by principal components of a training texture image. Experimental results show that the proposed method is effective in not only scale invariant texture classification including estimation of scale rate, but also scale invariant segmentation of 2D image for scene analysis.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Varma, M., Zisserman, A.: Estimating Illumination Direction from Textured Images. In: Proc. Int. Conf. Computer Vision and Pattern Recognition (CVPR 2003), vol. 1, pp. 179–186 (2004)
Turtinen, M., Pietikainen, M.: Visual Training and Classification of Outdoor Textured Scene Images. In: The 3rd international workshop on texture analysis and synthesis, pp. 101–106 (2003)
Pun, C., Lee, M.: Log-polar wavelet energy signatures for rotation and scale invariant texture classification. IEEE Trans. PAMI (Pattern Analysis and Machine Intelligence) 25(5), 590–602 (2003)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. PAMI 24(7), 971–987 (2002)
Jain, A.K., Duin, P., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Trans. PAMI 22(1), 4–37 (2000)
Fisher, R.A.: The Statistical Utilization of Multiple Measurements. Annals of Eugenics 8, 376–386 (1938)
Gibson, J.J.: Perception of the Visual World. HoughtonMifflin, Boston (1950)
Lindeberg, T.: Scale-space theory: A basic tool for analysing structures at different scales. J. of Applied Statistics 21(2), 224–270 (1994)
Lindeberg, T.: Scale-space for discrete signals. IEEE Trans. PAMI 12(3), 234–254 (1990)
Goudail, F., Lange, E., Iwamoto, T., Kyuma, K., Otsu, N.: Face Recognition System Using Local Autocorrelations and Multiscale Integration. IEEE Trans. PAMI 18(10), 1024–1028 (1996)
Popovici, V., Thiran, J.: Higher Order Autocorrelations for Pattern Classification. In: International Conference on Image Processing (ICIP), pp. 724–727 (2001)
Kreutz, M., Volpel, B., Janssen, H.: Scale-invariant image recognition based on higher-order autocorrelation features. Pattern Recognition 29(1), 19–26 (1996)
McLaughlin, J., Raviv, J.: Nth-Order Autocorrelations in Pattern Recognition. Information and Control 12, 121–142 (1968)
Fraleigh, J., Beauregard, R.: Linear Algebra. Addison-Wesley, Reading (1995)
Rao, C.R.: The Utilization of Multiple Measurements in Problems of Biological Classification. J. Royal Statistical Soc. ser. B 10, 159–203 (1948)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, London (1990)
Loog, M., Duin, R., Haeb-Umbach, R.: Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria. IEEE Trans. PAMI 23(7), 762–766 (2001)
Brodatz, P.: Textures: A Photographic Album for Artist and Designers. Dover, New York (1966)
Tsutsui, K., Sakato, H., Naganuma, T., Taira, M.: Neural Correlates for Perception of 3D surface Orientation from Texture Gradient. Science 298, 409–412 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kang, Y., Morooka, K., Nagahashi, H. (2005). Scale Invariant Texture Analysis Using Multi-scale Local Autocorrelation Features. In: Kimmel, R., Sochen, N.A., Weickert, J. (eds) Scale Space and PDE Methods in Computer Vision. Scale-Space 2005. Lecture Notes in Computer Science, vol 3459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408031_31
Download citation
DOI: https://doi.org/10.1007/11408031_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25547-5
Online ISBN: 978-3-540-32012-8
eBook Packages: Computer ScienceComputer Science (R0)