Recognizing materials in real-world images is a challenging 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 material databases, while
being more diverse and well-sampled across its 23 categories. Using MINC, we
train convolutional neural networks (CNNs) for two tasks: classifying materials
from patches, and simultaneous material recognition and segmentation in full
images. For patch-based classification 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 accuracy. Our
experiments demonstrate that having a large, well-sampled dataset such as MINC
is crucial for real-world material recognition and segmentation.

本文介绍了一个新的、大规模、开放的野外材料数据库（Materials in Context Database，MINC），并结合深度学习，在野外图像中实现材料识别和分割。使用 MINC，我们训练了卷积神经网络（CNN）进行两个任务：对补丁分类的材料分类和在完整图像中同时进行材料识别和分割。通过在 MINC 上进行补丁分类，我们发现表现最好的 CNN 架构可以实现 85.2% 的平均分类准确度。我们将这些训练好的 CNN 分类器转换为高效的完全卷积框架，再结合完全连接的条件随机场（CRF）来预测图像中每个像素的材料，达到 73.1% 的平均分类准确度。我们的实验表明，在野外材料识别和分割中，拥有一个大型的、经过充分采样的数据集（如 MINC）至关重要。