Scale-invariant image recognition based on higher-order autocorrelation features

摘要

We propose a framework and a complete implementation of a translation and scale-invariant image recognition system for natural indoor scenes. The system employs higher-order autocorrelation features of scale space data which permit linear classification. An optimal linear classification method is presented, which is able to cope with a large number of classes represented by many, as well as very few samples. In the course of the analysis of our system, we examine which numerical methods for feature transformation and classification show sufficient stability to fulfill these demands. The implementation has been extensively tested. We present the results of our own application and several classification benchmarks.