IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast parallel algorithm for thinning digital patterns
Communications of the ACM
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel feature extraction method and hybrid tree classification for handwritten numeral recognition
Pattern Recognition Letters
Spatial Representation of Dissimilarity Data via Lower-Complexity Linear and Nonlinear Mappings
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A Discussion on the Classifier Projection Space for Classifier Combining
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Handwritten Word Recognition based on Structural Characteristics and Lexical Support
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
A trainable feature extractor for handwritten digit recognition
Pattern Recognition
Reliable recognition of handwritten digits using a cascade ensemble classifier system and hybrid features
A SVM-based cursive character recognizer
Pattern Recognition
Character Recognition Using Hierarchical Vector Quantization and Temporal Pooling
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
International Journal on Document Analysis and Recognition
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An Efficient Tree Classifier Ensemble-Based Approach for Pedestrian Detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimal zoning design by genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Permutation Coding Technique for Image Recognition Systems
IEEE Transactions on Neural Networks
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One of the main problems in pattern recognition is obtaining the best set of features to represent the data. In recent years, several feature extraction algorithms have been proposed. However, due to the high degree of variability of the patterns, it is difficult to design a single representation that can capture the complex structure of the data. One possible solution to this problem is to use a multiple-classifier system (MCS) based on multiple feature representations. Unfortunately, still missing in the literature is a methodology for comparing and selecting feature extraction techniques based on the dissimilarity of the feature representations. In this paper, we propose a framework based on dissimilarity metrics and the intersection of errors, in order to analyze the relationships among feature representations. Each representation is used to train a classifier, and the results are compared by means of a dissimilarity metric. Then, with the aid of Multidimensional Scaling, visual representations are obtained of each of the dissimilarities and used as a guide to identify those that are either complementary or redundant. We applied the proposed framework to the problem of handwritten character and digit recognition. The analysis is followed by the use of an MCS built on the assumption that combining dissimilar feature representations can greatly improve the performance of the system. Experimental results demonstrate that a significant improvement in classification accuracy is achieved due to the complementary nature of the representations. Moreover, the proposed MCS obtained the best results to date for both the MNIST handwritten digit dataset and the Cursive Character Challenge (C-Cube) dataset.