The Random Subspace Method for Constructing Decision Forests
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
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
A discriminant analysis using composite features for classification problems
Pattern Recognition
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
Computers in Biology and 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
Texture descriptors for generic pattern classification problems
Expert Systems with Applications: An International Journal
Predictive vaccinology: optimisation of predictions using support vector machine classifiers
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Local phase quantization descriptor for improving shape retrieval/classification
Pattern Recognition Letters
Hi-index | 12.05 |
Presented in this paper is a novel feature extractor technique based on texture descriptors. Starting from the standard feature vector representation, we study different methods for representing a pattern as a matrix. Texture descriptors are then used to represent each pattern. We examine a variety of local ternary patterns and local phase quantization texture descriptors. Since these texture descriptors extract information using subwindows of the textures (i.e. a set of neighbor pixels), they handle the correlation among the original features (note that the pixels of the texture that describes a pattern are extracted starting from the original feature). We believe that our new technique exploits a new source of information. Our best approach using several well-known benchmark datasets, is obtained coupling the continuous wavelet approach for transforming a vector into a matrix and a variant of the local phase quantization based on a ternary coding for extracting the features from the matrix. Support vector machines are used both for the vector-based descriptors and the texture descriptors. Our experiments show that the texture descriptors along with the vector-based descriptors can be combined to improve overall classifier performance.