Material recognition is a fundamental problem in perception that is receiving increasing attention. Following the recent work using Flickr [16, 23] , we empirically study material recognition of real-world objects using a rich set of local features. We use the Kernel Descriptor framework [5] and extend the set of descriptors to include materialmotivated attributes using variances of gradient orientation and magnitude. Large-Margin Nearest Neighbor learning is used for a 30-fold dimension reduction. We improve the state-of-the-art accuracy on the Flickr dataset [16] from 45% to 54%. We also introduce two new datasets using ImageNet and macro photos, extensively evaluating our set of features and showing promising connections between material and object recognition.
Hu et al. REF extend the Kernel descriptors with variances of gradient orientations and magnitudes to handle materials.
Toward Robust Material Recognition for Everyday Objects
BMVC
Computer Science