Material recognition in the wild with the materials in context database

Recognizing materials in real-world images is a challeng-ing task. Real-world materials have rich surface texture, geometry, lighting conditions, and clutter, which combine to make the problem particularly difficult. In this paper, we introduce a new, large-scale, open dataset of materials in the wild, the Materials in Context Database (MINC), and combine this dataset with deep learning to achieve material recognition and segmentation of images in the wild. MINC is an order of magnitude larger than previous ma-terial databases, while being more diverse and well-sampled across its 23 categories. Using MINC, we train convolu-tional neural networks (CNNs) for two tasks: classifying materials from patches, and simultaneous material recogni-tion and segmentation in full images. For patch-based clas-sification on MINC we found that the best performing CNN architectures can achieve 85.2 % mean class accuracy. We convert these trained CNN classifiers into an efficient fully convolutional framework combined with a fully connected conditional random field (CRF) to predict the material at every pixel in an image, achieving 73.1 % mean class ac-curacy. Our experiments demonstrate that having a large, well-sampled dataset such as MINC is crucial for real-world material recognition and segmentation. 1