A Learning Algorithm for the Appearance- Based Recognition of Complex Objects

Abstract — This paper addresses the problem of recognizing complex objects in images. The proposed approach is based on a prototype-centered object representation which describes objects as sets of local features. During an evolutionary learning step the model is derived from a set of sample images. The proceeding of the training is measured with regard to the recognition rate as well as the coverage of the training samples. The proposed method is tested by the recognition of 14 classes of cars in highway scenes. A classification rate of 98 percent is achieved. Index Terms — Image processing, object recognition, decision trees, genetic algorithms