Learning rotation invariant convolutional filters for texture classification
This addresses texture classification for computer vision applications, but it is incremental as it builds on existing shallow CNN methods.
The paper tackles the problem of texture classification with varying orientations by learning rotation invariant convolutional filters using a shallow CNN, achieving results comparable to state-of-the-art while reducing parameters by an order of magnitude.
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.