Deep convolutional network with locality and sparsity constraints for texture classification

Recent studies have demonstrated advantages of the representations learned by Convolutional Neural Networks (CNNs) in providing an appealing paradigm for visual classification tasks. Most existing methods adopt activations from the last fully connected layer as the image representation. This paper advocates exploiting appropriately convolutional layer activations to constitute a powerful descriptor for texture classification under an end-to-end learning framework. The main component of our method is a new locality-aware coding layer conducted with the locality constraint, where the dictionary and the encoding representation are learned simultaneously. The layer is readily amenable to training via the backpropagation as the locality-aware coding process has an analytical solution. It is capable of capturing class-specific information which makes the learned convolutional features more robust. The resulting representation is particularly useful for texture classification. Comprehensive experiments on the DTD, FMD and KTH-T2b datasets show that our approach notably outperforms the state-of-the-art methods.