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 diffi cult. 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 MINe, 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 peiforming 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.
More recently, the Materials in Context Database (MINC) REF brought the data size to an even larger scale with 3 million patches classified into 23 materials.
Material recognition in the wild with the Materials in Context Database
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science