Paper Info
Title
Transfer Learning for Material Classification using Convolutional Networks.
Abstract
Material classification in natural settings is a challenge due to complex interplay of geometry, reflectance properties, and illumination. Previous work on material classification relies strongly on hand-engineered features of visual samples. In this work we use a Convolutional Neural Network (convnet) that learns descriptive features for the specific task of material recognition. Specifically, transfer learning from the task of object recognition is exploited to more effectively train good features for material classification. The approach of transfer learning using convnets yields significantly higher recognition rates when compared to previous state-of-the-art approaches. We then analyze the relative contribution of reflectance and shading information by a decomposition of the image into its intrinsic components. The use of convnets for material classification was hindered by the strong demand for sufficient and diverse training data, even with transfer learning approaches. Therefore, we present a new data set containing approximately 10k images divided into 10 material categories.
Year
Venue
Field
2016
arXiv: Computer Vision and Pattern Recognition
Training set,Material classification,Pattern recognition,Convolutional neural network,Computer science,Transfer of learning,Artificial intelligence,Reflectivity,Machine learning,Cognitive neuroscience of visual object recognition
DocType
Volume
Citations 
Journal
abs/1609.06188
0
PageRank 
References 
Authors
0.34
9
2
Name
Order
Citations
PageRank
Patrick Wieschollek1263.21
Hendrik P. A. Lensch2147196.59