Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA
Pattern Recognition Letters
Non-iterative generalized low rank approximation of matrices
Pattern Recognition Letters
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Matrix-pattern-oriented least squares support vector classifier with AdaBoost
Pattern Recognition Letters
A reliable method for cell phenotype image classification
Artificial Intelligence in Medicine
Tornado detection with support vector machines
ICCS'03 Proceedings of the 2003 international conference on Computational science
Enhanced local texture feature sets for face recognition under difficult lighting conditions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Matrix representation in pattern classification
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
In this paper we propose a new feature extractor technique for pattern classification that is based on the calculation of texture descriptors. Starting from the standard feature vector representation, we rearrange the patterns as matrices and then apply such standard texture descriptor techniques as local binary patterns, local ternary patterns, and Coiflet wavelets. In our classification experiments using several well-known benchmark datasets, support vector machines are used both for the vector-based descriptors and the texture descriptors. Using our new feature extractor technique, the feature vector is arranged as a matrix by random assignment. For each pattern, 50 different random assignments are performed, and then the classification results are combined using the mean rule. We believe that our novel technique introduces a new source of information. Our experiments show that the texture descriptors along with the vector-based descriptors can be combined to improve overall classifier performance. In our experimental results the performance obtained by our extraction technique outperformed that obtained by support vector machines trained using standard vector-based descriptors.