Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Classification of Partial 2-D Shapes Using Fourier Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape discrimination and classification in robotic vision using scaled normalized central moments
Proceedings of the IFAC workshop on Mutual impact of computing power and control theory
Least Squares and Estimation Measures via Error Correcting Output Code
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Relationship of Sum and Vote Fusion Strategies
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Fourier Descriptors for Plane Closed Curves
IEEE Transactions on Computers
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A novel methodology is herein outlined for combining the classification decisions of different neural network classifiers. Instead of the usual approach for applying voting schemes on the decisions of their output layer neurons, the proposed methodology integrates higher order features extracted by their upper hidden layer units. More specifically, different instances (cases) of each such classifier, derived from the same training process but with different training parameters, are investigated in terms of their higher order features, through similarity analysis, in order to find out repeated and stable higher order features. Then, all such higher order features are integrated through a second stage neural network classifier having as inputs suitable similarity features of them. The herein suggested hierarchical neural system for pattern recognition shows improved classification performance in a computer vision task. The validity of this novel combination approach has been investigated when the first stage neural classifiers involved correspond to different Feature Extraction Methodologies (FEM) for shape classification. The experimental study illustrates that such an approach, integrating higher order features through similarity analysis of a committee of the same classifier instances (cases) and a second stage neural classifier, outperforms other combination methods, like voting combination schemes as well as single neural network classifiers having as inputs all FEMs derived features. In addition, it outperforms hierarchical combination methods non performing integration of cases through similarity analysis.