Comparative study of texture feature for rotation invariant RECOGNITION

Human visual system easily and rapidly recognizes a scene or image under different affine transformations, which is not the true for the machine. Rotation is more complex than translation and engenders more difficulties in analysis. This paper address evaluation and comparison of texture descriptors, particularly Local Relational String, under rotation effects. Many methods are invariant for geometric transformation, but this is not sufficient to handle the classification problem. We show in this study, when training samples represent a large range of rotated textures, methods with high discriminative properties leads to a very good classification rate despite their no invariance for rotation.

[1]  J. Robson,et al.  Application of fourier analysis to the visibility of gratings , 1968, The Journal of physiology.

[2]  Calvin C. Gotlieb,et al.  Texture descriptors based on co-occurrence matrices , 1990, Comput. Vis. Graph. Image Process..

[3]  P. Bolon,et al.  Analyse d'images: filtrage et segmentation , 1995 .

[4]  Wilson S. Geisler,et al.  Texture segmentation using Gabor modulation/demodulation , 1987, Pattern Recognit. Lett..

[5]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Christophe Rosenberger,et al.  Texture analysis of an image by using a rotation-invariant model , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[7]  S. Zucker.  Toward a model of texture , 1976 .

[8]  R. Nevatia,et al.  Structural Texture Analysis Applications , 1982 .

[9]  Rama Chellappa,et al.  Classification of textures using Gaussian Markov random fields , 1985, IEEE Trans. Acoust. Speech Signal Process..

[10]  Larry S. Davis,et al.  An Empirical Evaluation of Generalized Cooccurrence Matrices , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Edward J. Delp,et al.  Segmentation of textured images using a multiresolution Gaussian autoregressive model , 1999, IEEE Trans. Image Process..

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

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

[14]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[15]  M. R. Turner,et al.  Texture discrimination by Gabor functions , 1986, Biological Cybernetics.

[16]  Bertrand Zavidovique,et al.  Local Relational String for Textures Classification , 2006, 2006 International Conference on Image Processing.

[17]  Narendra Ahuja,et al.  Shape From Texture: Integrating Texture-Element Extraction and Surface Estimation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Sara J. Graves,et al.  Using Association Rules as Texture Features , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  J. Ronsin,et al.  Comparison Between Cooccurrence Matrices, Local Histograms And Curvilinear Integration For Texture Characterization , 1986, Other Conferences.

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

[21]  Ian Burns,et al.  Measuring texture classification algorithms , 1997, Pattern Recognit. Lett..

[22]  Anil K. Jain,et al.  Texture Analysis , 2018, Handbook of Image Processing and Computer Vision.

[23]  L. F. Pau,et al.  Handbook of pattern recognition & computer vision , 1993 .