Our world consists not only of objects and scenes but also of materials of various kinds. Being able to recognize the materials that surround us (e.g., plastic, glass, concrete) is important for humans as well as for computer vision systems. Unfortunately, materials have received little attention in the visual recognition literature, and very few computer vision systems have been designed specifically to recognize materials. In this paper, we present a system for recognizing material categories from single images. We propose a set of low and mid-level image features that are based on studies of human material recognition, and we combine these features using an SVM classifier. Our system outperforms a state-of-the-art system (Varma and Zisserman, TPAMI 31(11):2032–2047, 2009) on a challenging database of real-world material categories (Sharan et al., J Vis 9(8):784–784a, 2009). When the performance of our system is compared directly to that of human observers, humans outperform our system quite easily. However, when we account for the local nature of our image features and the surface properties they measure (e.g., color, texture, local shape), our system rivals human performance. We suggest that future progress in material recognition will come from: (1) a deeper understanding of the role of non-local surface properties (e.g., extended highlights, object identity); and (2) efforts to model such non-local surface properties in images.
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In this paper, we use the terms “local features” and “non-local features” relative to the size of the surface of interest and not the size of the image. The images we will consider in this paper correspond to the spatial scale depicted in Fig. 3b. For this scale, features such as color, texture, and local shape are considered local features, whereas features such as outline shape and object identity are considered non-local features.
For the spatial scales depicted in FMD images, object properties such as outline shape are “non-local” in nature. Meanwhile, local image properties such as color or texture can vary across the surface of interest, and hence, they are “local” in nature.
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Sharan, L., Liu, C., Rosenholtz, R. et al. Recognizing Materials Using Perceptually Inspired Features. Int J Comput Vis 103, 348–371 (2013). https://doi.org/10.1007/s11263-013-0609-0
- Material recognition
- Material classification
- Texture classification
- Mechanical Turk