Enhancing fractal descriptors on images by combining boundary and interior of Minkowski dilation
This is an incremental improvement for texture image classification, addressing a limitation in classical fractal descriptors.
The authors tackled the problem of improving fractal descriptors for gray-level texture images by combining both interior (volume) and boundary (area) measures from Minkowski dilation, resulting in enhanced classification performance on benchmark databases.
This work proposes to obtain novel fractal descriptors from gray-level texture images by combining information from interior and boundary measures of the Minkowski dilation applied to the texture surface. At first, the image is converted into a surface where the height of each point is the gray intensity of the respective pixel in that position in the image. Thus, this surface is morphologically dilated by spheres. The radius of such spheres is ranged within an interval and the volume and the external area of the dilated structure are computed for each radius. The final descriptors are given by such measures concatenated and subject to a canonical transform to reduce the dimensionality. The proposal is an enhancement to the classical Bouligand-Minkowski fractal descriptors, where only the volume (interior) information is considered. As different structures may have the same volume, but not the same area, the proposal yields to more rich descriptors as confirmed by results on the classification of benchmark databases.