Texture classification via extended local graph structure

Abstract In this paper, we propose a simple and robust local descriptor operator, called the extended local graph structure (ELGS). The original local graph structure (LGS) performs very well in many domains, for instance face recognition, face spoofing detection and others. However, LGS has a few demerits such as LGS is not robust to the noise present in the image and LGS takes into considerations the horizontal graph and ignores the vertical graph which causes a loss in the spatial information. Therefore, we extend the idea of LGS by encoding the pattern into two directions. This is means that we take into consideration the vertical graph along with the horizontal graph and then concatenate the two computed histograms features to form a global descriptor. Experimental results on the UIUC and XU High Resolution texture databases show a promising performance.

[1]  Cedric Nishan Canagarajah,et al.  Robust rotation invariant texture classification , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  Tze Hui Liew,et al.  Real Time Face tracker based on Local Graph Structure Threshold (LGS-TH) , 2013 .

[3]  Cordelia Schmid,et al.  A sparse texture representation using local affine regions , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  J. Duvernoy.  Optical-digital processing of directional terrain textures invariant under translation, rotation, and change of scale. , 1984, Applied optics.

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

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

[7]  Housam Khalifa Bashier,et al.  Plant Identification Based on Leaf Shape and Texture Pattern Using Local Graph Structure (LGS) , 2013 .

[8]  Matti Pietikäinen,et al.  Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Matti Pietikäinen,et al.  RLBP: Robust Local Binary Pattern , 2013, BMVC.

[10]  Housam Khalifa Bashier,et al.  Face recognition using local graph theory (LGT) , 2013, 2013 INTERNATIONAL CONFERENCE ON COMPUTING, ELECTRICAL AND ELECTRONIC ENGINEERING (ICCEEE).

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

[12]  Yong Xu,et al.  Scale-space texture description on SIFT-like textons , 2012, Comput. Vis. Image Underst..

[13]  Ville Ojansivu,et al.  Blur Insensitive Texture Classification Using Local Phase Quantization , 2008, ICISP.

[14]  Manik Varma,et al.  Locally Invariant Fractal Features for Statistical Texture Classification , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[15]  Fatimah Khalid,et al.  Face Detection Based on Graph Structure and Neural Networks , 2012 .

[16]  Jianping Yin,et al.  Local binary pattern (LBP) and local phase quantization (LBQ) based on Gabor filter for face representation , 2013, Neurocomputing.

[17]  Eimad E. A. Abusham,et al.  Face Recognition Using Local Graph Structure (LGS) , 2011, HCI.

[18]  J. Hossen,et al.  Illumination Normalization using Eimad-housam Technique , 2012 .

[19]  Siong Hoe Lau,et al.  Face Spoofing Detection Using Local Graph Structure , 2014, INFOCOM 2014.

[20]  Andrew Zisserman,et al.  A Statistical Approach to Texture Classification from Single Images , 2005, International Journal of Computer Vision.

[21]  Andrew Zisserman,et al.  A Statistical Approach to Material Classification Using Image Patch Exemplars , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.