Estimation of Fractal Dimension in Different Color Model

Estimation of Fractal Dimension in Different Color Model

Sumitra Kisan (Veer Surendra Sai University of Technology, Burla, India), Sarojananda Mishra (Indira Gandhi Institute of Technology, Sarang, Dhenkanal, India), Ajay Chawda (Veer Surendra Sai University of Technology, Burla, India) and Sanjay Nayak (Veer Surendra Sai University of Technology, Burla, India)
Copyright: © 2018 |Pages: 19
DOI: 10.4018/IJKDB.2018010106


This article describes how the term fractal dimension (FD) plays a vital role in fractal geometry. It is a degree that distinguishes the complexity and the irregularity of fractals, denoting the amount of space filled up. There are many procedures to evaluate the dimension for fractal surfaces, like box count, differential box count, and the improved differential box count method. These methods are basically used for grey scale images. The authors' objective in this article is to estimate the fractal dimension of color images using different color models. The authors have proposed a novel method for the estimation in CMY and HSV color spaces. In order to achieve the result, they performed test operation by taking number of color images in RGB color space. The authors have presented their experimental results and discussed the issues that characterize the approach. At the end, the authors have concluded the article with the analysis of calculated FDs for images with different color space.
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Mandelbrot in 1982 has invented fractal theory that provides both mathematical and descriptive model for several apparently complex structures present in nature (B. B. Mandelbrot, 1982). Irregular shapes for example; clouds, mountains and coastlines are not simply defined by traditional geometry (Euclidean geometry). They often possess a significant invariance with the changes of magnification. This nature of self-similarity is a crucial eminence of fractal in nature. Usually it is used to estimate fractal dimension (FD). It provides several mathematical models for complex real-world objects. Fractal analysis has excessive impact in digital image analyses. It is extended by many more concepts applicable to a broader class of fractals. Fractal geometry is one of the best areas for doing research. As this area is vast, many researchers have worked on different problems and given their contribution for estimating fractal dimension. After the term fractal geometry has been introduced, several researchers shared their knowledge on this field and because of their effortless job various methodologies have been introduced to calculate fractal dimension. In 1986 Gangepain and Roques Carmes stated a method called reticular cell counting technique (Gangepain, & Roques-Carmes, 1986) and this method is enhanced by Voss in 1986 (Ford & Roberts, 1998) discussed about the method of probability. Pentland suggested the Fourier power spectrum of image intensity surface for fractal dimension estimation (Pentland, 1984). Keller et al. also proposed Reticular cell counting method and this one is the improved version of the previous reticular cell counting (1989). In 1986, Sarkar and Chaudhuri suggested a well-organized methodology to estimate the fractal dimension known as the Differential Box Counting (DBC) (Sarker & Chaudhuri, 1994). Instead of using the process of cell counting method to count the number of boxes, they used minimum and maximum grays levels of the corresponding grid in the image for calculation known as Differential Box Count Method (DBC). Jian, Qia and Caixin jointly enounced the improved Differential Box Counting or improved DBC (2009). In the improved DBC the drawbacks of DBC, Box height calculation, Box number calculation, partition of image intensity surface was analyzed and improved. The mean and standard deviation of intensity of a block was used for calculation of box height. The minimum gray level was used in the box height calculation rather than gray level 0. The image intensity partition occurs resulting in zero distance of neighboring blocks.

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