  We propose a Deep Texture Encoding Network (Deep-TEN) with a novel Encoding
Layer integrated on top of convolutional layers, which ports the entire
dictionary learning and encoding pipeline into a single model. Current methods
build from distinct components, using standard encoders with separate
off-the-shelf features such as SIFT descriptors or pre-trained CNN features for
material recognition. Our new approach provides an end-to-end learning
framework, where the inherent visual vocabularies are learned directly from the
loss function. The features, dictionaries and the encoding representation for
the classifier are all learned simultaneously. The representation is orderless
and therefore is particularly useful for material and texture recognition. The
Encoding Layer generalizes robust residual encoders such as VLAD and Fisher
Vectors, and has the property of discarding domain specific information which
makes the learned convolutional features easier to transfer. Additionally,
joint training using multiple datasets of varied sizes and class labels is
supported resulting in increased recognition performance. The experimental
results show superior performance as compared to state-of-the-art methods using
gold-standard databases such as MINC-2500, Flickr Material Database,
KTH-TIPS-2b, and two recent databases 4D-Light-Field-Material and GTOS. The
source code for the complete system are publicly available.
