Texture classification using invariant features of local textures

In this paper, the authors present a texture descriptor algorithm called invariant features of local textures (IFLT). IFLT generates scale, rotation and (essentially) illumination invariant descriptors from a small neighbourhood of pixels around a centre pixel or a texture patch. Texture classification experiments were carried out on the Brodatz, Outex and KTH-TIPS2 databases. Demonstrated texture classification accuracy exceeds the previously published state of the art at a significantly lower computational cost. Experiments also suggests that IFLT descriptors are in a sense intuitive texture descriptors.

[1]  Phil Brodatz,et al.  Textures: A Photographic Album for Artists and Designers , 1966 .

[2]  Larry S. Davis,et al.  Texture Analysis Using Generalized Co-Occurrence Matrices , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[4]  Larry S. Davis,et al.  Polarograms: A new tool for image texture analysis , 1979, Pattern Recognit..

[5]  D. Chetverikov EXPERIMENTS IN THE ROTATION-INVARIANT TEXTURE DISCRIMINATION USING ANISOTROPY FEATURES. , 1982 .

[6]  R A Young,et al.  The Gaussian derivative model for spatial vision: I. Retinal mechanisms. , 1988, Spatial vision.

[7]  J. M. Hans du Buf,et al.  A review of recent texture segmentation and feature extraction techniques , 1993 .

[8]  Pietro Perona,et al.  Rotation invariant texture recognition using a steerable pyramid , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[9]  R. Porter,et al.  Robust rotation-invariant texture classification: wavelet, Gabor filter and GMRF based schemes , 1997 .

[10]  W. Lam,et al.  Rotated texture classification by improved iterative morphological decomposition , 1997 .

[11]  H. Arof,et al.  Circular neighbourhood and 1-D DFT features for texture classification and segmentation , 1998 .

[12]  Shree K. Nayar,et al.  Reflectance and texture of real-world surfaces , 1999, TOGS.

[13]  Miodrag Popovic,et al.  Texture analysis using 2D wavelet transform: theory and applications , 1999, 4th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services. TELSIKS'99 (Cat. No.99EX365).

[14]  S. Mallat VI – Wavelet zoom , 1999 .

[15]  B. S. Manjunath,et al.  Rotation-invariant texture classification using a complete space-frequency model , 1999, IEEE Trans. Image Process..

[16]  Tieniu Tan,et al.  Brief review of invariant texture analysis methods , 2002, Pattern Recognit..

[17]  Andrew Zisserman,et al.  Classifying Images of Materials: Achieving Viewpoint and Illumination Independence , 2002, ECCV.

[18]  Matti Pietikäinen,et al.  Outex - new framework for empirical evaluation of texture analysis algorithms , 2002, Object recognition supported by user interaction for service robots.

[19]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Andrew Zisserman,et al.  Texture classification: are filter banks necessary? , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[21]  Keun Ho Ryu,et al.  New shape-based texture descriptors for rotation invariant texture classification , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[22]  Matti Pietikäinen,et al.  View-based recognition of real-world textures , 2004, Pattern Recognit..

[23]  Barbara Caputo,et al.  Class-Specific Material Categorisation , 2005, ICCV.