Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification using multiresolution Markov random field models
Pattern Recognition Letters
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Histograms and Their Use for Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison and fusion of multiresolution features for texture classification
Pattern Recognition Letters
Multiresolution histograms for SVM-Based texture classification
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Scale invariant texture analysis using multi-scale local autocorrelation features
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Rotation-invariant texture classification using a complete space-frequency model
IEEE Transactions on Image Processing
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
Scale-space representation of lung HRCT images for diffuse lung disease classification
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Nonlinear scale space theory in texture classification using multiple classifier systems
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
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Since texture is scale dependent, multi-scale techniques are quite useful for texture classification. Scale-space theory introduces multi-scale differential operators. In this paper, the N-jet of derivatives up to the second order at different scales is calculated for the textures in Brodatz album to generate the textures in multiple scales. After some preprocessing and feature extraction using principal component analysis (PCA), instead of combining features obtained from different scales/derivatives to construct a combined feature space, the features are fed into a two-stage combined classifier for classification. The learning curves are used to evaluate the performance of the proposed texture classification system. The results show that this new approach can significantly improve the performance of the classification especially for small training set size. Further, comparison between combined feature space and combined classifiers shows the superiority of the latter in terms of performance and computation complexity.