  Material attributes have been shown to provide a discriminative intermediate
representation for recognizing materials, especially for the challenging task
of recognition from local material appearance (i.e., regardless of object and
scene context). In the past, however, material attributes have been recognized
separately preceding category recognition. In contrast, neuroscience studies on
material perception and computer vision research on object and place
recognition have shown that attributes are produced as a by-product during the
category recognition process. Does the same hold true for material attribute
and category recognition? In this paper, we introduce a novel material category
recognition network architecture to show that perceptual attributes can, in
fact, be automatically discovered inside a local material recognition
framework. The novel material-attribute-category convolutional neural network
(MAC-CNN) produces perceptual material attributes from the intermediate pooling
layers of an end-to-end trained category recognition network using an auxiliary
loss function that encodes human material perception. To train this model, we
introduce a novel large-scale database of local material appearance organized
under a canonical material category taxonomy and careful image patch extraction
that avoids unwanted object and scene context. We show that the discovered
attributes correspond well with semantically-meaningful visual material traits
via Boolean algebra, and enable recognition of previously unseen material
categories given only a few examples. These results have strong implications in
how perceptually meaningful attributes can be learned in other recognition
tasks.
