Combining multiple neural nets for visual feature selection and classification

We present a system for object recognition in real images employing three different types of neural networks which accomplish feature extraction and classification. The main advantages of the method are its portability to different object domains without extensive parameter adjustments or changes in the feature extraction, and the low computational effort. This is achieved using a combination of the vector quantization, principal component analysis and a network for nonlinear classification tasks.