Deep TEN: Texture Encoding Network

Understand

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.