Three-layer perceptron based classifiers for the partial shape classification problem

摘要

The question of classification robustness in the multi-network neural network based system for the partial shape classification problem is addressed. In order to increase the robustness in classification, an extension of the multi-network system and a new single network system are proposed. The extension increases the robustness by augmenting the training of the three-layer perceptrons in the system. The three-layer perceptron in the single network system is designed to detect the features in all of the pattern classes. In the test mode, the test pattern is hypothesized to belong to the pattern classes and the network response to the test pattern is used to determine the similarity scores for the hypothesized classes. Two partial shape classification experiments are designed to compare the performance of the original multinetwork system, the augmented training approach, and the single network system on exactly the same test set. The results indicate that there is a significant increase in the classification robustness in the proposed augmented training approach and the single network system.