Use of IFS Codes for Learning 2D Isolated-Object Classification Systems

Abstract

Automatic recognition of complex images is a hard and computationally expensive task, mainly because it is extremely difficult to capture in an automatic way and with a few features the necessary discriminant information. If such features were available, a proper learning system could be trained to distinguish images of different kinds of objects, starting from a set of labeled examples. In this paper we show that fractal features obtained from Iterated Function System encodings capture the kind of information that is needed by learning systems and, thus, allow the successful classification of 2-dimensional images of objects. We also present a fractal feature extraction algorithm and report the classification results obtained on two very different test-beds by applying Machine Learning techniques to sets of encoded images.