The nature of statistical learning theory
The nature of statistical learning theory
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Blur Insensitive Texture Classification Using Local Phase Quantization
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Diagnosis of valvular heart disease through neural networks ensembles
Computer Methods and Programs in Biomedicine
Aggregation of classifiers for staining pattern recognition in antinuclear autoantibodies analysis
IEEE Transactions on Information Technology in Biomedicine
Evaluation of ensemble methods for diagnosing of valvular heart disease
Expert Systems with Applications: An International Journal
Color texture image segmentation based on neutrosophic set and wavelet transformation
Computer Vision and Image Understanding
Feature extraction based on co-occurrence of adjacent local binary patterns
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Extended local binary patterns for texture classification
Image and Vision Computing
Early experiences in mitotic cells recognition on HEp-2 slides
CBMS '10 Proceedings of the 2010 IEEE 23rd International Symposium on Computer-Based Medical Systems
Color to grayscale staining pattern representation in IIF
CBMS '11 Proceedings of the 2011 24th International Symposium on Computer-Based Medical Systems
Noise tolerant local binary pattern operator for efficient texture analysis
Pattern Recognition Letters
Discriminative features for texture description
Pattern Recognition
Efficient image appearance description using dense sampling based local binary patterns
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
| Hi-index | 12.05 |
In this work we propose an ensemble of texture descriptors for HEp-2 cell classification. Our system is based on a ''pyramidal application'' of local binary patterns coupled with a method for handling nonuniform bins. This feature extraction approach is then combined with a support vector machine (SVM) classifier. We test our method on a recent contest dataset (the MIVIA HEp-2 images dataset) using different testing protocols. This dataset is very challenging since the images are characterized by high variability in illumination. Therefore, to obtain good results, it is essential to apply a preprocessing algorithm: we choose the histogram equalization. We found that the best results are obtained when the original intensity images are converted into grayscale images with ten discrete values. Since a training set is provided in the contest dataset, we use it for descriptor selection and for parameter settings. The system built by using the training data is then applied to the testing set. Experiments show that our method outperforms the winner of the recent contest at the 21st International Conference on Pattern Recognition 2012. Our descriptors and MATLAB code will be available at webpage http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_pass=124&preview=.