  Recognizing material from color images is still a challenging problem today.
While deep neural networks provide very good results on object recognition and
has been the topic of a huge amount of papers in the last decade, their
adaptation to material images still requires some works to reach equivalent
accuracies. Nevertheless, recent studies achieve very good results in material
recognition with deep learning and we propose, in this paper, to review most of
them by focusing on three aspects: material image datasets, influence of the
context and ad hoc descriptors for material appearance. Every aspect is
introduced by a systematic manner and results from representative works are
cited. We also present our own studies in this area and point out some open
challenges for future works.
