Texture Segmentation Using Voronoi Polygons
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to wavelets
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Reduced Multidimensional Co-Occurrence Histograms in Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Using Association Rules as Texture Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Picture Processing
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Mammogram Classification Using a Multi-Resolution Pattern Recognition Approach
SIBGRAPI '01 Proceedings of the 14th Brazilian Symposium on Computer Graphics and Image Processing
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multi-resolution Image Parametrization in Stepwise Diagnostics of Coronary Artery Disease
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Towards symbolic mining of images with association rules: Preliminary results on textures
Intelligent Data Analysis - Analysis of Symbolic and Spatial Data
Rotation-invariant texture classification using a complete space-frequency model
IEEE Transactions on Image Processing
Segmentation of textured images using a multiresolution Gaussian autoregressive model
IEEE Transactions on Image Processing
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Automated Marsh-like classification of celiac disease in children using local texture operators
Computers in Biology and Medicine
Supporting diagnostics of coronary artery disease with neural networks
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Image processing and machine learning for fully automated probabilistic evaluation of medical images
Computer Methods and Programs in Biomedicine
| Hi-index | 0.00 |
In the paper an innovative alternative to automatic image parametrization on multiple resolutions, based on texture description with specialized association rules, and image evaluation with machine learning methods is presented. The algorithm ArTex for parameterizing textures with association rules belonging to structural parametrization algorithms was developed. In order to improve the classification accuracy a multiresolution approach is used. The algorithm ARes for finding more informative resolutions based on the SIFT algorithm is described. The presented algorithms are evaluated on several public domains and the results are compared to other well-known parametrization algorithms belonging to statistical and spectral parametrization algorithms. Significant improvement of classification results was observed when combining parametrization attributes at several image resolutions for most parametrization algorithms. Our results show that multiresolution image parametrization should be considered when improvement of classification accuracy in textural domains is required. These resolutions have to be selected carefully and may depend on the domain itself.