Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Training Invariant Support Vector Machines
Machine Learning
Reflectance and Texture of Real-World Surfaces Authors
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Class-Specific Material Categorisation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Adapting visual category models to new domains
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Learning people detection models from few training samples
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Evaluation of image features using a photorealistic virtual world
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Sorted Random Projections for robust texture classification
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Due to the strong impact of machine learning methods on visual recognition, performance on many perception task is driven by the availability of sufficient training data. A promising direction which has gained new relevance in recent years is the generation of virtual training examples by means of computer graphics methods in order to provide richer training sets for recognition and detection on real data. Success stories of this paradigm have been mostly reported for the synthesis of shape features and 3D depth maps. Therefore we investigate in this paper if and how appearance descriptors can be transferred from the virtual world to real examples. We study two popular appearance descriptors on the task of material categorization as it is a pure appearance-driven task. Beyond this initial study, we also investigate different approach of combining and adapting virtual and real data in order to bridge the gap between rendered and real-data. Our study is carried out using a new database of virtual materials VIPS that complements the existing KTH-TIPS material database.