Gaussian Markov random field based improved texture descriptor for image segmentation

This paper proposes a novel robust texture descriptor based on Gaussian Markov random fields (GMRFs). A spatially localized parameter estimation technique using local linear regression is performed and the distributions of local parameter estimates are constructed to formulate the texture features. The inconsistencies arising in localized parameter estimation are addressed by applying generalized inverse, regularization and an estimation window size selection criterion. The texture descriptors are named as local parameter histograms (LPHs) and are used in texture segmentation with the k-means clustering algorithm. The segmentation results on general texture datasets demonstrate that LPH descriptors significantly improve the performance of classical GMRF features and achieve better results compared to the state-of-the-art texture descriptors based on local feature distributions. Impressive natural image segmentation results are also achieved and comparisons to the other standard natural image segmentation algorithms are also presented. LPH descriptors produce promising texture features that integrate both statistical and structural information about a texture. The region boundary localization can be further improved by integrating colour information and using advanced segmentation algorithms. Display Omitted Distributions of GMRF local parameter estimates as improved texture descriptors.Spatially localized parameter estimation using local linear regression.Approaches to overcome inconsistencies in localized parameter estimation.Proposed descriptors capture both spatial dependencies and distributions of texture.

[1]  Matti Pietikäinen,et al.  A Framework for Analyzing Texture Descriptors , 2008, VISAPP.

[2]  Shu Liao,et al.  Texture Classification by using Advanced Local Binary Patterns and Spatial Distribution of Dominant Patterns , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[3]  Jana Reinhard,et al.  Textures A Photographic Album For Artists And Designers , 2016 .

[4]  Rama Chellappa,et al.  Multiresolution Gauss-Markov random field models for texture segmentation , 1997, IEEE Trans. Image Process..

[5]  Hailin Jin,et al.  A Locally Linear Regression Model for Boundary Preserving Regularization in Stereo Matching , 2012, ECCV.

[6]  Lauge Sørensen,et al.  Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns , 2010, IEEE Transactions on Medical Imaging.

[7]  Antonio Bosnjak,et al.  Medical images segmentation using Gabor filters applied to echocardiographic images , 1998, Computers in Cardiology 1998. Vol. 25 (Cat. No.98CH36292).

[8]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[9]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[10]  Maya R. Gupta,et al.  Adaptive Local Linear Regression With Application to Printer Color Management , 2008, IEEE Transactions on Image Processing.

[11]  David A. Clausi,et al.  Gaussian MRF rotation-invariant features for image classification , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Sang Uk Lee,et al.  New MRF Parameter Estimation Technique for Texture Image Segmentation using Hierarchical GMRF Model Based on Random Spatial Interaction and Mean Field Theory , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[13]  Liangpei Zhang,et al.  Classification of High Spatial Resolution Imagery Using Improved Gaussian Markov Random-Field-Based Texture Features , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Michal Haindl,et al.  Texture segmentation benchmark , 2008, 2008 19th International Conference on Pattern Recognition.

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

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

[17]  E.E. Pissaloux,et al.  Image Processing , 1994, Proceedings. Second Euromicro Workshop on Parallel and Distributed Processing.

[18]  Erkki Oja,et al.  Texture discrimination with multidimensional distributions of signed gray-level differences , 2001, Pattern Recognit..

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

[20]  Bede Liu,et al.  On the use of singular value decomposition and decimation in discrete-time band-limited signal extrapolation , 1984 .

[21]  Mohamed Cheriet,et al.  Gray-level Texture Characterization Based on a New Adaptive Nonlinear Auto-Regressive Filter , 2008 .

[22]  H. Künsch.  Gaussian Markov random fields , 1979 .

[23]  DeLiang Wang,et al.  Texture segmentation using Gaussian-Markov random fields and neural oscillator networks , 2001, IEEE Trans. Neural Networks.

[24]  Martin J. Wainwright,et al.  Image denoising using scale mixtures of Gaussians in the wavelet domain , 2003, IEEE Trans. Image Process..

[25]  Maria Petrou,et al.  Image processing - dealing with texture , 2020 .

[26]  Steve R. Gunn,et al.  Snake based unsupervised texture segmentation using Gaussian Markov Random Field Models , 2011, 2011 18th IEEE International Conference on Image Processing.

[27]  S. K. Nayar,et al.  Multiresolution Histograms and their Use for Texture Classification , 2003 .

[28]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[29]  Jixian Dong,et al.  Paper web defection segmentation using Gauss-Markov random field texture features , 2011, 2011 International Conference on Image Analysis and Signal Processing.

[30]  Patrick Pérez,et al.  Interactive Image Segmentation Using an Adaptive GMMRF Model , 2004, ECCV.

[31]  Tieniu Tan,et al.  Invariant texture segmentation via circular Gabor filters , 2002, Object recognition supported by user interaction for service robots.

[32]  Leonhard Held,et al.  Gaussian Markov Random Fields: Theory and Applications , 2005 .

[33]  Xavier Descombes,et al.  Estimating Gaussian Markov random field parameters in a nonstationary framework: application to remote sensing imaging , 1999, IEEE Trans. Image Process..

[34]  Hossein Mobahi,et al.  Segmentation of Natural Images by Texture and Boundary Compression , 2011, International Journal of Computer Vision.

[35]  S. Annadurai,et al.  Color Image Segmentation using Adaptive Spatial Gaussian Mixture Model , 2010 .

[36]  J. Preston.  Ξ-filters , 1983 .

[37]  Allen Y. Yang,et al.  Unsupervised segmentation of natural images via lossy data compression , 2008, Comput. Vis. Image Underst..

[38]  Zhenhua Guo,et al.  Rotation invariant texture classification using LBP variance (LBPV) with global matching , 2010, Pattern Recognit..

[39]  R. Penrose.  On best approximate solutions of linear matrix equations , 1956, Mathematical Proceedings of the Cambridge Philosophical Society.

[40]  J. Besag,et al.  On conditional and intrinsic autoregressions , 1995 .

[41]  Matti Pietikäinen,et al.  Rotation-invariant texture classification using feature distributions , 2000, Pattern Recognit..

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

[43]  Yehoshua Y. Zeevi,et al.  Integrated active contours for texture segmentation , 2006, IEEE Transactions on Image Processing.

[44]  Sasan Mahmoodi,et al.  Quantitative analysis of pulmonary emphysema using isotropic Gaussian Markov random fields , 2014, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

[45]  Mihai Datcu,et al.  Bayesian selection of the neighbourhood order for Gauss-Markov texture models , 2002, Pattern Recognit. Lett..

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

[47]  DeLiang Wang,et al.  Texture classification using spectral histograms , 2003, IEEE Trans. Image Process..

[48]  Sasan Mahmoodi,et al.  Unsupervised Texture Segmentation using Active Contours and Local Distributions of Gaussian Markov Random Field Parameters , 2012, BMVC.

[49]  Shree K. Nayar,et al.  Reflectance and Texture of Real-World Surfaces Authors , 1997, CVPR 1997.

[50]  Yu Peng,et al.  A unified model of GMRF and MOG for image segmentation , 2005, IEEE International Conference on Image Processing 2005.

[51]  Aleksandra Mojsilovic,et al.  Image segmentation by spatially adaptive color and texture features , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

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

[53]  Rongchun Zhao,et al.  Adaptive Segmentation of Textured Images by Using the Coupled Markov Random Field Model , 2006, IEEE Transactions on Image Processing.

[54]  Lei Yu,et al.  Urban extraction from SAR images using local statistical characteristics and gaussian markov random field mod , 2006, 2006 8th international Conference on Signal Processing.

[55]  Peyman Milanfar,et al.  Kernel Regression for Image Processing and Reconstruction , 2007, IEEE Transactions on Image Processing.

[56]  Oscar Nestares,et al.  Texture segmentation through adaptive filtering , 2007 .

[57]  Xavier Bresson,et al.  Fast Texture Segmentation Based on Semi-Local Region Descriptor and Active Contour , 2009 .

[58]  Mark S. Nixon,et al.  Texture Segmentation by Evidence Gathering , 2011 .

[59]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[60]  Rama Chellappa,et al.  Unsupervised Texture Segmentation Using Markov Random Field Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[61]  Song-Chun Zhu,et al.  Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling , 1998, International Journal of Computer Vision.

[62]  I. J. Myung,et al.  Tutorial on maximum likelihood estimation , 2003 .

[63]  Shree K. Nayar,et al.  Reflectance and texture of real-world surfaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[64]  Stan Z. Li.  Markov Random Field Modeling in Image Analysis , 2009, Advances in Pattern Recognition.