earticle

영어

The Classifier Integration Model (CIM) with Convolutional Neural Networks (CNNs) as its local classifiers is applied to an object classification task for vehicle safety system in this paper. The Convolutional Neural Networks adopted in this paper has a very unique advantage when compared with conventional neural network models because CNNs do not require any feature extraction procedure prior to the classification process while other existing classification methods require rather very complex feature extraction process. Several CNN models are first designed as local classifiers and these models are then combined to make a decision in Classifier Integration Model for our classification task. Experiments on real data sets obtained for our experiments show that the CNN-based CIM scheme gives a promising performance in terms of training speed and classification accuracy.