text: We propose a method for integration of features extracted using deep representations of Convolutional Neural Networks (CNNs) each of which is learned using a different image dataset of objects and materials for material recognition. Given a set of representations of multiple pre-trained CNNs, we first compute activations of features using the representations on the images to select a set of samples which are best represented by the features. Then, we measure the uncertainty of the features by computing the entropy of class distributions for each sample set. Finally, we compute the contribution of each feature to representation of classes for feature selection and integration. We examine the proposed method on three benchmark datasets for material recognition. Experimental results show that the proposed method achieves state-of-the-art performance by integrating deep features. Additionally, we introduce a new material dataset called EFMD by extending Flickr Material Database (FMD). By the employment of the EFMD with transfer learning for updating the learned CNN models, we achieve 84.0 + -1.8 accuracy on the FMD dataset which is close to human performance that is 84.9 .
  @cite_5: 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 (, 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.
  @cite_17: It is easy to visually distinguish a ceramic knife from one made of steel, a leather jacket from one made of denim, and a plush toy from one made of plastic. Most studies of material appearance have focused on the estimation of specific material properties such as albedo or surface gloss, and as a consequence, almost nothing is known about how we recognize material categories like leather or plastic. We have studied judgments of high-level material categories with a diverse set of real-world photographs, and we have shown (Sharan, 2009) that observers can categorize materials reliably and quickly. Performance on our tasks cannot be explained by simple differences in color, surface shape, or texture. Nor can the results be explained by observers merely performing shape-based object recognition. Rather, we argue that fast and accurate material categorization is a distinct, basic ability of the visual system.
summary: Surface properties of materials, and the relationship between perception of material and object categories are analyzed in @cite_5 @cite_17 . They first proposed a well-designed benchmark dataset called FMD. Then, they designed descriptors to extract hand-crafted features for representation of various surface properties such as color, texture and shape for material recognition in @cite_5 . Moreover, they analyzed the relationship between object and material recognition for accurate and fast perception of materials in @cite_17 .