On the Behavior of Artificial Neural Network Classifiers in High-Dimensional Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Automated sub-cellular phenotype classification: an introduction and recent results
WISB '06 Proceedings of the 2006 workshop on Intelligent systems for bioinformatics - Volume 73
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Fusion of systems for automated cell phenotype image classification
Expert Systems with Applications: An International Journal
Local binary patterns variants as texture descriptors for medical image analysis
Artificial Intelligence in Medicine
Combining different local binary pattern variants to boost performance
Expert Systems with Applications: An International Journal
Texture descriptors for generic pattern classification problems
Expert Systems with Applications: An International Journal
Survey on LBP based texture descriptors for image classification
Expert Systems with Applications: An International Journal
Matrix representation in pattern classification
Expert Systems with Applications: An International Journal
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Objective: Image-based approaches have proven to be of great utility in the automated cell phenotype classification, it is very important to develop a method that efficiently quantifies, distinguishes and classifies sub-cellular images. Methods and materials: In this work, the invariant locally binary patterns (LBP) are applied, for the first time, to the classification of protein sub-cellular localization images. They are tested on three image datasets (available for download), in conjunction with support vector machines (SVMs) and random subspace ensembles of neural networks. Our method based on invariant LBP provides higher accuracy than other well-known methods for feature extraction; moreover, our method does not require to (direct) crop the cells for the classification. Results and conclusion: The experimental results show that the random subspace ensemble of neural networks outperforms the SVM in this problem. The proposed approach based on the solely LBP features gives accuracies of 85%, 93.9% and 88.4% on the 2D HeLa dataset, LOCATE endogenous and transfected datasets, respectively, and in combination with other state-of-the-art methods for the cell phenotype image classification we obtain a classification accuracy of 94.2%, 98.4% and 96.5%.