Paskaš Milorad P, Reljin Irini S, Reljin Branimir D
School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11121 Belgrade, Serbia ; Innovation Center of School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11121 Belgrade, Serbia.
School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11121 Belgrade, Serbia.
ScientificWorldJournal. 2014 Jan 22;2014:894546. doi: 10.1155/2014/894546. eCollection 2014.
This paper proposes two local multifractal measures motivated by blanket method for calculation of fractal dimension. They cover both fractal approaches familiar in image processing. The first two measures (proposed Methods 1 and 3) support model of image with embedded dimension three, while the other supports model of image embedded in space of dimension three (proposed Method 2). While the classical blanket method provides only one value for an image (fractal dimension) multifractal spectrum obtained by any of the proposed measures gives a whole range of dimensional values. This means that proposed multifractal blanket model generalizes classical (monofractal) blanket method and other versions of this monofractal approach implemented locally. Proposed measures are validated on Brodatz image database through texture classification. All proposed methods give similar classification results, while average computation time of Method 3 is substantially longer.
本文提出了两种受覆盖法启发的局部多重分形测度,用于分形维数的计算。它们涵盖了图像处理中常见的两种分形方法。前两种测度(所提出的方法1和方法3)支持嵌入维数为3的图像模型,而另一种支持嵌入在三维空间中的图像模型(所提出的方法2)。经典的覆盖法只为一幅图像提供一个值(分形维数),而通过所提出的任何一种测度获得的多重分形谱给出了一系列维数值。这意味着所提出的多重分形覆盖模型推广了经典的(单分形)覆盖法以及这种局部实现的单分形方法的其他版本。所提出的测度通过纹理分类在布罗达茨图像数据库上得到了验证。所有提出的方法给出了相似的分类结果,而方法3的平均计算时间长得多。