Interest Operator versus Gabor filtering for facial imagery classification
Pattern Recognition Letters
Improving the interest operator for face recognition
Expert Systems with Applications: An International Journal
A novel modular neural network for imbalanced classification problems
Pattern Recognition Letters
A novel Bayesian learning method for information aggregation in modular neural networks
Expert Systems with Applications: An International Journal
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Pattern Recognition Letters
Application of self-organizing feature neural network for target feature extraction
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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A modular neural network classifier has been applied to the problem of automatic target recognition using forward-looking infrared (FLIR) imagery. The classifier consists of several independently trained neural networks. Each neural network makes a decision based on local features extracted from a specific portion of a target image. The classification decisions of the individual networks are combined to determine the final classification. Experiments show that decomposition of the input features results in performance superior to a fully connected network in terms of both network complexity and probability of classification. Performance of the classifier is further improved by the use of multiresolution features and by the introduction of a higher level neural network on the top of the individual networks, a method known as stacked generalization. In addition to feature decomposition, we implemented a data-decomposition classifier network and demonstrated improved performance. Experimental results are reported on a large set of real FLIR images