Multifractal Analysis and Texture Classification

Dear Colleagues,

A common image processing method is texture classification, which determines the characteristics of textures by detecting spatial variations in picture amplitude.  Multifractal analysis can be used to perform this assessment. Multifractal analysis assesses the behavior of structure function logarithms within fine-scale bounds and deals with the scaling behaviors of measure distributions in a geometrical and statistical manner. Traditional generalizations of fractal systems based on a range of singularity exponents and multifractal spectra develop higher resolution images, which typically allow more precise texture classification; however, multifractal studies have been greatly hampered by the precise image scale. Estimating bias results from the abundance of coefficients available to perform linear regressions at small sizes. However, when some of these coefficients are excluded, the estimation variance rises. Signal self-similarity, wide probability distributions, multiplicative processes, unusual events, and texture characterization can all be studied through multifractal analysis. Although multifractal analysis techniques have been proposed in the past, it is now possible to enhance the scaling function, which is a parameter that results from multifractal analysis, by speeding up computation, thus improving accuracy. Moreover, 2D multifractal studies have seldomly been used in the past to perform texture characterizations.

This Special Issue aims to promote new research in the fields of characterization, analysis and classification of textures using multifractal systems, machine learning signatures, and static and dynamic measurements, and it will provide a platform for the publication of research papers that discuss their applications in characterization texture analysis and intrusion detection systems.

Dr. Smain Femmam
Guest Editor

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