The application of fractal analysis to feature extraction

As the interest in fractal geometry rises, the applications are getting more and more numerous in many domains. The aim of the authors is that these concepts can also be applied to feature extraction of patterns and that they can help, to a certain extent, to ease the solution of many problems. In this paper, the proposed method reduces the dimensionality of a two-dimensional pattern by way of a central projection approach, and thereafter, performs Daubechies´ wavelet transformation on the derived one-dimensional pattern to generate a set of wavelet transformation sub-patterns, namely, curves that are non-self-intersecting. Further from the resulting nonself-intersecting curves, the divider dimensions are computed with modified box-counting approach. These divider dimensions constitute a new feature vector for the original two-dimensional pattern, defined over the curve´s fractal dimensions.