A Noise-Aware Coding Scheme for Texture Classification

Texture-based analysis of images is a very common and much discussed issue in the fields of computer vision and image processing. Several methods have already been proposed to codify texture micro-patterns (texlets) in images. Most of these methods perform well when a given image is noise-free, but real world images contain different types of signal-independent as well as signal-dependent noises originated from different sources, even from the camera sensor itself. Hence, it is necessary to differentiate false textures appearing due to the noises, and thus, to achieve a reliable representation of texlets. In this proposal, we define an adaptive noise band (ANB) to approximate the amount of noise contamination around a pixel up to a certain extent. Based on this ANB, we generate reliable codes named noise tolerant ternary pattern (NTTP) to represent the texlets in an image. Extensive experiments on several datasets from renowned texture databases, such as the Outex and the Brodatz database, show that NTTP performs much better than the state-of-the-art methods.

[1]  Donald A. Adjeroh,et al.  Efficient texture analysis of SAR imagery , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Thomas W. Parks,et al.  Image denoising using total least squares , 2006, IEEE Transactions on Image Processing.

[3]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  L. Nanni,et al.  Selecting the Best Performing Rotation Invariant Patterns in Local Binary/Ternary Patterns , 2010, IPCV.

[6]  Matti Pietikäinen,et al.  A discriminative feature space for detecting and recognizing faces , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

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

[8]  Eui Chul Lee,et al.  New Finger Biometric Method Using Near Infrared Imaging , 2011, Sensors.

[9]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Wilson S. Geisler,et al.  Multichannel Texture Analysis Using Localized Spatial Filters , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[12]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

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

[14]  Yunqian Ma,et al.  Event detection using local binary pattern based dynamic textures , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[15]  Marko Heikkilä,et al.  A texture-based method for modeling the background and detecting moving objects , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[17]  Max Q.-H. Meng,et al.  Computer-Aided Detection of Bleeding Regions for Capsule Endoscopy Images , 2009, IEEE Transactions on Biomedical Engineering.

[18]  Shaogang Gong,et al.  Facial expression recognition based on Local Binary Patterns: A comprehensive study , 2009, Image Vis. Comput..

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

[20]  Jean Ponce,et al.  General Road Detection From a Single Image , 2010, IEEE Transactions on Image Processing.

[21]  James R. Janesick.  CCD Transfer Curves and Optimization , 2001 .

[22]  Gwen Littlewort,et al.  Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction. , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[23]  W. James MacLean,et al.  CCD noise removal in digital images , 2006, IEEE Transactions on Image Processing.

[24]  Dennis F. Dunn,et al.  Optimal Gabor filters for texture segmentation , 1995, IEEE Trans. Image Process..

[25]  Glenn Healey,et al.  Radiometric CCD camera calibration and noise estimation , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

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

[27]  Youngbae Hwang,et al.  Sensor noise modeling using the Skellam distribution: Application to the color edge detection , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Loris Nanni,et al.  Local binary patterns variants as texture descriptors for medical image analysis , 2010, Artif. Intell. Medicine.

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

[30]  Shaozi Li,et al.  Structured local binary Haar pattern for pixel-based graphics retrieval , 2010 .

[31]  Andreas Uhl,et al.  Automated Marsh-like classification of celiac disease in children using local texture operators , 2011, Comput. Biol. Medicine.

[32]  Fátima N. S. de Medeiros,et al.  Wavelet Analysis for Wind Fields Estimation , 2010, Sensors.

[33]  Shu Liao,et al.  Dominant Local Binary Patterns for Texture Classification , 2009, IEEE Transactions on Image Processing.