Combined Feature Extraction and Selection in Texture Analysis

Texture analysis is an important research content in pattern recognition and computer vision, and we can get important information from the image through it. As an important method in feature extraction and classification, texture analysis has a very wide range of applications in the field of scientific research and engineering technology. In order to solve the problem of image classification, feature extraction and selection are combined in texture analysis. Different methods of texture analysis are used to extract texture features and a texture feature selection method based on grouped sorting is proposed in this paper. As in that work, 42 different texture feature parameters are extracted and efficient less ones are selected by the proposed method to be used in the classification. The proposed method is tested in numerical experiments on the texture database and real tumor data set. The experimental results suggest that the method is effective.

[1]  Pasi Luukka,et al.  Feature selection using fuzzy entropy measures with similarity classifier , 2011, Expert Syst. Appl..

[2]  Md. Khayrul Bashar,et al.  Wavelet transform-based locally orderless images for texture segmentation , 2003, Pattern Recognit. Lett..

[3]  Gerald M. Flachs,et al.  Markov random field texture models for classification , 1990, Defense, Security, and Sensing.

[4]  R. Sukanesh A Padma,et al.  Automatic Classification and Segmentation of Brain Tumor in CT Images using Optimal Dominant Gray level Run length Texture Features , 2011 .

[5]  Anil K. Jain,et al.  Texture classification and segmentation using multiresolution simultaneous autoregressive models , 1992, Pattern Recognit..

[6]  J Hong,et al.  GRAY LEVEL-GRADIENT COOCCURRENCE MATRIX TEXTURE ANALYSIS METHOD , 1984 .

[7]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  C. A. Murthy,et al.  Unsupervised Feature Selection Using Feature Similarity , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

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

[10]  Alfian Abdul Halin,et al.  Multi-Class Support Vector Machines For Texture Classification Using Gray-Level Histogram And Edge Detection Features , 2016 .

[11]  A. Kassner,et al.  Texture Analysis: A Review of Neurologic MR Imaging Applications , 2010, American Journal of Neuroradiology.

[12]  Anil K. Jain,et al.  Markov Random Field Texture Models , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[14]  Kee Tung. Wong,et al.  Texture features for image classification and retrieval. , 2002 .

[15]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 2002, IEEE Trans. Image Process..

[16]  Jie Zhao,et al.  Texture Image Classification Using Radon Transform-Based Invariant Moments , 2013 .

[17]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[18]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 1999, Proceedings 10th International Conference on Image Analysis and Processing.