We present a method for learning discriminative filters using a shallow Convolutional Neural Network (CNN). We encode rotation invariance directly in the model by tying the weights of groups of filters to several rotated versions of the canonical filter in the group. These filters can be used to extract rotation invariant features well-suited for image classification. We test this learning procedure on a texture classification benchmark, where the orientations of the training images differ from those of the test images. We obtain results comparable to the state-of-the-art. Compared to standard shallow CNNs, the proposed method obtains higher classification performance while reducing by an order of magnitude the number of parameters to be learned.
@article{arxiv.1604.06720,
title = {Learning rotation invariant convolutional filters for texture classification},
author = {Diego Marcos and Michele Volpi and Devis Tuia},
journal= {arXiv preprint arXiv:1604.06720},
year = {2017}
}