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Reduced Multidimensional Co-Occurrence Histograms in Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Advances in kernel methods: support vector learning
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Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
A kernel view of the dimensionality reduction of manifolds
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Pattern Recognition
Texture classification using Gabor wavelets based rotation invariant features
Pattern Recognition Letters
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IEEE Transactions on Computers
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Plant texture classification using gabor co-occurrences
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
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In the context of texture classification, this article explores the capacity and the performance of some combinations of feature extraction, linear and nonlinear dimensionality reduction techniques and several kinds of classification methods. The performances are evaluated and compared in term of classification error. In order to test our texture classification protocol, the experiment carried out images from two different sources, the well known Brodatz database and our leaf texture images database.