Viewpoint Invariant Texture Description Using Fractal Analysis

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Describing Textures in the Wild (2013, methodology): “This experiments evaluates the encodings on several material recognition datasets: CUReT [9], UMD [39], UIUC [18], KTH-TIPS [14], KTH-TIPS2(a and b) [7], and FMD [30].”
Deep Filter Banks for Texture Recognition, Description, and Segmentation (2015, background): “UMD [108] consists of 1000 images, spread across 25 classes, but was collected in uncontrolled conditions.”
Towards Viewpoint Invariant 3D Human Pose Estimation (2016, background): “Other features such as viewpoint invariant 3D feature maps [43], histograms of 3D joint locations [63], multifractal spectrum [64], volumetric attention models [28], and volumetric convolutional filters [45,46] have been proposed for 3D modeling.”
A Training-free Classification Framework for Textures, Writers, and Materials (2012, methodology): “Alternatively, [14] uses modified SIFT and intensity domain SPIN images, [13] plain SIFT, [6, 20, 23] variants of LBP descriptors, [31] a grayscale image patch for its Joint descriptor and an MRF representation; [36] local fractals, and [18, 19] (sorted) random projections over small patches.”
From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018, methodology): “The basic MFS method was proposed in [253], where MFS was ﬁrst deﬁned for simple image features, such as intensity, gradient and Laplacian of Gaussian (LoG).”