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Visual textures have played a key role in image understanding because theyconvey important semantics of images, and because texture representations thatpool local image descriptors in an orderless manner have had a tremendousimpact in diverse applications. In this paper we make several contributions totexture understanding. First, instead of focusing on texture instance andmaterial category recognition, we propose a human-interpretable vocabulary oftexture attributes to describe common texture patterns, complemented by a newdescribable texture dataset for benchmarking. Second, we look at the problem ofrecognizing materials and texture attributes in realistic imaging conditions,including when textures appear in clutter, developing corresponding benchmarkson top of the recently proposed OpenSurfaces dataset. Third, we revisit classictexture representations, including bag-of-visual-words and the Fisher vectors,in the context of deep learning and show that these have excellent efficiencyand generalization properties if the convolutional layers of a deep model areused as filter banks. We obtain in this manner state-of-the-art performance innumerous datasets well beyond textures, an efficient method to apply deepfeatures to image regions, as well as benefit in transferring features from onedomain to another.

Deep filter banks for texture recognition, description, and segmentation