A novel method to estimate fractal dimension of color images

Preparatory Experiments Regarding Human Brain Perception and Reasoning of Image Complexity for Synthetic Color Fractal and Natural Texture Images via EEG

“…Would novelty and structural organization influence complexity perception? So far, the complexity of fractal color images is mathematically expressed by various measures [3][4][5], with the most common being the fractal dimension and entropy [3,[6][7][8] or by metrics based on image segmentation [9]. An interesting approach for evaluating visual complexity, suitable for creative works such as paintings, uses an objective metric derived from neuroscience termed Artistic Complexity which looks into the average mutual information of different sub-parts of the images [10,11].…”

Color Texture Image Complexity—EEG-Sensed Human Brain Perception vs. Computed Measures

“…In this sense, so far researchers have investigated mathematical and image analysis measures [36][37][38][39][40], human subjective evaluations of textural complexity [33,34,41] or separately and seldom, the perception detected within the neural activity [42,43]. Because humans are the final observers of the textures, it seems mandatory to infer information from the human evaluation of complexity [34,41,44,45], which may go beyond the textural image properties.…”

“…Such investigations, which allow for experimentally comparing the textural complexity that is expressed by image analysis measures and the human perception of complexity or sensation induced by the observation of a colored textured surface image, as expressed by subjective scoring and complemented by neural activity responses, are less frequent (e.g., [43], and we further present one approach in this research. Among the existing mathematical assessments of spatio-chromatic complexity of textures [36][37][38]40,46], such as Independent Component Analysis, which incorporates the sparsity of data in the complexity measure [39], mutual information within regions [47], and so on, the entropy and fractal dimension analysis are the most commonly used. The entropy measure is directly correlated to the surface complexity, as given by colors; while the fractal dimension is associated to the auto-similarity texture property, viewed as a ratio of the change in detail, in relation to the change in scale.…”

Use of fractals to measure anisotropy in point patterns extracted with the DPT of an image

“…In the past, the fractal dimension has been investigated in image analysis, specifically texture analysis. The theoretical development has increased as computing grows (Chen et al, 1993;Ida and Sambonsugi, 1998;Kisan et al, 2016;Lam and Li, 2010;Liu, 2008;Melnikov, 2007;Rigaut et al, 1998;Rosen, 1995;Wang et al, 2011;Zhao and Liu, 2005). It has been used in remote sensing (Al-Saidi and Abdul-Wahed, 2018;Berizzi et al, 2001;Chenoweth et al, 1995;Lam, 1990;Zhu and Yang, 2010;Di Martino et al, 2010;Riccio et al, 2014;Sawada et al, 2001), image inpainting (Xiu-hong and Bao-long, 2009;Bai et al, 2011), image matching with texture (Dolez and Vincent, 2007), denoising (Ghazel et al, 2003;Malviya, 2008), restoration (Hamano et al, 1996), segmentation (Ida and Sambonsugi, 1998), compression (Ismail et al, 2010;Jiang, 1995), shape classification and segmentation (Kisan et al, 2016;Nayak et al, 2015), interpolation (Shi et al, 2008), classification (Shih, 2008), superresolution (Wee and Shin, 2010), medical imaging (Hong and Huidong, 2012;Priya et al, 2011;Qi et al, 2009;Tang and Wang, 2006) and specifically in texture analysis (Avadhanam and Mitra, 1994;Costa et al, 2012;…”