Classifying materials in the real world

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COCO-Stuff: Thing and Stuff Classes in Context (2016, background): “Early stuff datasets [6, 15, 34, 9] focused on texture classification and had simple images completely covered with a single textured patch.”
Extended local binary patterns for texture classification (2012, methodology): “The samples are then classi ﬁ ed according to their normalized histogram feature vectors h i and h i , using χ 2 distance metric the same distance metric used in [2, 3, 27, 30].”
Pairwise Rotation Invariant Co-Occurrence Local Binary Pattern (2014, methodology): “[44], instead of nearest neighbor classifier, used kernel SVM as classifier”
Recognizing Materials Using Perceptually Inspired Features (2013, background): “2008; Romeiro and Zickler 2010) and the recognition of 3-D textures (Dana et al. 1999; Dana and Nayar 1998; Cula and Dana 2004a; Nillius and Eklundh 2004; Caputo et al. 2005; Varma and Zisserman 2005; Caputo et al. 2007; Varma and Zisserman 2009).”
A Training-free Classification Framework for Textures, Writers, and Materials (2012, methodology): “KTHTIPS2b is a very challenging texture benchmark.”