The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.01 - January (2008 vol.30)
pp: 52-61
ABSTRACT
Textures within real images vary in brightness, contrast, scale and skew as imaging conditions change. To enable recognition of textures in real images, it is necessary to employ a similarity measure which is invariant to these properties. Furthermore, since textures often appear on undulating surfaces, such invariances must necessarily be local rather than global. Despite these requirements, it is only relatively recently that texture recognition algorithms with local scale and affine invariance properties have begun to be reported. Typically, they comprise detecting feature points followed by geometric normalization prior to description. We describe a method based on invariant combinations of linear filters. Unlike previous methods, we introduce a novel family of filters, which provide scale invariance, resulting in a texture description invariant to local changes in orientation, contrast and scale and robust to local skew. Significantly, the family of filters enable local scale invariants to be defined without using a scale selection principle or a large number of filters. A texture discrimination method based on the ?2 similarity measure applied to histograms derived from our filter responses outperforms existing methods for retrieval and classification results for both the Brodatz textures and the UIUC database, which has been designed to require local invariance.
CITATION
Matthew Mellor, Byung-Woo Hong, Michael Brady, "Locally Rotation, Contrast, and Scale Invariant Descriptors for Texture Analysis", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.30, no. 1, pp. 52-61, January 2008, doi:10.1109/TPAMI.2007.1161
REFERENCES
[1] C.-H. Pun and M.-C. Lee, “Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp.590-602, May 2003.
[2] J. Zhang and T. Tan, “Affine Invariant Classification and Retrieval of Texture Images,” Pattern Recognition, vol. 36, pp. 657-664, 2003.
[3] T. Leung and J. Malik, “Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons,” Int'l J. Computer Vision, vol. 43, no. 1, pp. 29-44, 2001.
[4] M. Varma and A. Zisserman, “Classifying Images of Materials: Achieving Viewpoint and Illumination Independence,” Proc. Seventh European Conf. Computer Vision, vol. 3, pp. 255-271, 2002.
[5] S. Lazebnik, C. Schmidt, and J. Ponce, “A Sparse Texture Recognition Using Local Affine Regions,” Technical Report CVR-TR-2004-01, Beckman Inst., Univ. of Illi nois, 2004.
[6] S. Blunsden and L. Attalah, “Investigating the Effects of Scale in MRF Texture Classification,” Proc. IEE Int'l Conf. Visual Information Eng., 2005.
[7] T. Tuytelaars and L. Van Gool, “Wide Baseline Stereo Matching Based on Local, Affinely Invariant Regions,” Proc. 11th British Machine Vision Conf., pp. 412-425, 2000.
[8] F. Schaffalitzky and A. Zisserman, “Multi-View Matching for Unordered Image Sets,” Proc. Seventh European Conf. Computer Vision, pp. 414-431, 2002.
[9] C. Schmidt, “Constructing Models for Content-Based Image Retrieval,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 39-45, 2001.
[10] T. Randen and J.H. Husøy, “Filtering for Texture Classification: A Comparative Study,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 291-310, Apr. 1999.
[11] A. Laine and J. Fan, “Texture Classification by Wavelet Packet Signatures,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, pp. 1186-1190, 1993.
[12] T. Chang and C.-C.J. Kuo, “Texture Analysis and Classification with Tree-Structured Wavelet Transform,” IEEE Trans. Image Processing, vol. 2, no. 4, pp. 429-441, 1993.
[13] W.T. Freeman and E.H. Adelson, “The Design and Use of Steerable Filters,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 9, pp. 891-906, Sept. 1991.
[14] B.M. ter Haar Romeny, Front-End Vision and Multi-Scale Image Analysis. Kluwer Academic, 2003.
[15] T. Lindeberg, “Feature Detection with Automatic Scale Selection,” Int'l J. Computer Vision, vol. 30, no. 2, pp. 77-116, 1998.
[16] T. Lindeberg and J. Gårding, “Direct Computation of Shape Cues Using Scale-Adapted Spatial Derivative Operators,” Int'l J.Computer Vision, vol. 17, no. 2, 1996.
[17] E.H. Adelson, E.P. Simoncelli, W.T. Freeman, and D.J. Heeger, “Shiftable Multiscale Transforms,” IEEE Trans. Information Theory, vol. 38, no. 2, 1992.
[18] M. Felsberg, “Low-Level Image Processing with the Structure Multivector,” PhD dissertation, Christian-Albrechts-Universität zu Kiel, 2002.
[19] U. Köthe, “Integrated Edge and Junction Detection with the Boundary Tensor,” Proc. Ninth IEEE Int'l Conf. Computer Vision, vol. 1, pp. 424-431, 2003.
[20] J. Koenderink and A. van Doorn, “Surface Shape and Curvature Scales,” Image and Vision Computing, vol. 10, pp. 557-565, 1992.
[21] G. Carneiro and A.D. Jepson, “Phase-Based Local Features,” Proc. Seventh European Conf. Computer Vision, vol. 1, pp. 282-296, 2002.
[22] J. Malik and P. Perona, “Preattentive Texture Discrimination with Early Vision Mechanisms,” J. Optical Soc. Am., vol. 7, no. 5, pp.923-932, 1990.
[23] J.A. Noble, D. Boukerroui, and M. Brady, “On the Choice of Band-Pass Quadrature Filters,” J. Math. Imaging and Vision, vol. 21, no. 1, pp. 53-80, 2004.
[24] P. Brodatz, Textures: A Photographic Album for Artists and Designers. Dover Publications, 1966.
[25] M. Varma and A. Zisserman, “Texture Classification: Are Filter Banks Necessary?” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2003.
24 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool