Texture processing of synthetic aperture radar data using second-order spatial statistics
Computers & Geosciences
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal sampling of Gabor features for face recognition
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Simplified Gabor wavelets for human face recognition
Pattern Recognition
Locally Rotation, Contrast, and Scale Invariant Descriptors for Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture analysis with variational hidden Markov trees
IEEE Transactions on Signal Processing - Part I
Extended fractal analysis for texture classification and segmentation
IEEE Transactions on Image Processing
Adaptive Segmentation of Textured Images by Using the Coupled Markov Random Field Model
IEEE Transactions on Image Processing
SVD-Based Modeling for Image Texture Classification Using Wavelet Transformation
IEEE Transactions on Image Processing
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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The classification of texture images, especially those with spatial rotation and region shift, is a challenge and important problem in image analysis and classification. This paper proposes a novel algorithm design, an ellipse invariant algorithm, to improve the capability of texture classification for spatial rotation and region shift. The principle of an ellipse invariant algorithm is to use a minimum ellipse to enclose specific representative pixels extracted by the subtracting clustering method. After translating the coordinates, the ellipse in the rotated texture would be formulated as the ellipse in original texture. Also in this paper a hybrid texture filter is proposed. In the hybrid texture filter the scheme of texture feature extraction include Gabor wavelet, neighboring grey level dependence matrix and the ellipse invariant algorithm. Support vector machines (SVMs) are introduced as the classifier. The proposed hybrid texture filter can classify both the stochastic textures and structural textures. Experimental results reveal that this proposed algorithm outperforms existing design algorithms.