Automatically Discovering Local Visual Material Attributes

Gabriel Schwartz, Ko Nishino; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3565-3573

Shape cues play an important role in computer vision, but shape is not the only information available in images. Materials, such as fabric and plastic, are discernible in images even when shapes, such as those of an object, are not. We argue that it would be ideal to recognize materials without relying on object cues such as shape. This would allow us to use materials as a context for other vision tasks, such as object recognition. Humans are intuitively able to find visual cues that describe materials. While previous frameworks attempt to recognize these cues (as visual material traits), they rely on a fully-supervised set of training image patches. This requirement is not feasible when multiple annotators and large quantities of images are involved. In this paper, we derive a framework that allows us to discover locally-recognizable material attributes from crowdsourced perceptual material distances. We show that the attributes we discover do in fact separate material categories. Our learned attributes exhibit the same desirable properties as material traits, despite the fact that they are discovered using only partial supervision.