Enriching Texture Analysis with Semantic Data

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Describing Textures in the Wild (2013, methodology): “Using IFV SIFT as an underlying representation for our attributes, we obtain 49.82% mAP on the retrieval experiment of [22], which is is not as good as their result with LBP u (63.3%).”
The Synthesizability of Texture Examples (2014, background): “Features for material classification include statistics of filter responses [20, 28, 34], joint intensity distributions within a compact neighborhood [36, 22], geometric features over topographic maps [38], and high-level semantic attributes [29].”
Fine-Grained Visual Comparisons with Local Learning (2014, background): “In texture recognition, relative attributes could capture the strength of base properties [26].”
From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018, methodology): “Nevertheless, there are only several papers [17, 34, 151] investigating the texture attributes thus far, and there is no systematic study yet attempted.”