Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reduced Multidimensional Co-Occurrence Histograms in Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments with a featureless approach to pattern recognition
Pattern Recognition Letters - special issue on pattern recognition in practice V
Filtering for Texture Classification: A Comparative Study
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
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Dissimilarity-based classification of spectra: computational issues
Real-Time Imaging - Special issue on spectral imaging
A Maximum Likelihood Approach to Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Description Through Histograms of Equivalent Patterns
Journal of Mathematical Imaging and Vision
Hi-index | 0.00 |
An industrial rock classification system is constructed and studied. The local texture information in many image patches is extracted and classified. The decisions made at the local level are fused to form the high-level decision on the image/rock as a whole. The main difficulties of this application lay in significant variability and inhomogeneity of local textures caused by uneven rock surfaces and intrusions. Therefore, an emphasis is paid to the derivation of informative representation of local texture and to robust classification algorithms. The study focuses on the co-occurrence representation of texture comparing the two frequently used strategies, namely the approach based on Haralick features and methods utilizing directly the co-occurrence likelihoods. Apart of maximum-likelihood (ML) classifiers also an alternative method is studied considering the likelihoods to prototypes as feature of a new space. Unlike the ML methods, a classifier built in this space may leverage all training examples. It is experimentally illustrated, that in the rock classification setup the methods directly using the co-occurrence estimates outperform the feature-based techniques.