Multiresolution codes for scene categorization

The development of fast and reliable image classification algorithms is mandatory for modern image applications involving large databases. Biological systems seem to have the ability to categorize complex scenes in an accurate and very fast way. Our aim is to develop an architecture that leads to similar performances in computer vision. In this work, we present a coding method based on some principles inspired from biology that achieves a fast classification of complex visual scenes. A signature vector is extracted from the visual scene by a multi-scale filtering obtained through a bank of Gabor filters. These vectors constitute the inputs of a radial basis function network. The first connection layer implements a recoding of the filter outputs. The second one achieves a linear separation of the classes in the space of coding. We showed that an incremental approach in which each class is learned separately outperforms a more global one in which we tried to learn all classes together. According to the considered image category, the subset of features leading to the best result could be different, suggesting the use of feature vectors adapted to each image category. However, one of the major results of our study is that the signature vector we used, albeit very simple to compute, contains enough information to allow a correct image classification.