Recognizing Materials Using Perceptually Inspired Features
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Adelson_Recognizing materials.pdf
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Author(s)
Sharan, Lavanya
Liu, Ce
Rosenholtz, Ruth
Adelson, Edward H.
Date Issued
February 2013
Journal
International Journal of Computer Vision
Publisher
Springer-Verlag
Citation
Sharan, Lavanya, Ce Liu, Ruth Rosenholtz, and Edward H. Adelson. “Recognizing Materials Using Perceptually Inspired Features.” Int J Comput Vis 103, no. 3 (February 19, 2013): 348–371.
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/s11263-013-0609-0
