Graph-based Isometry Invariant Representation Learning
This addresses the challenge of geometric transformation sensitivity in computer vision, offering increased resiliency to data variability, though it is incremental as it builds on graph-based methods for invariance.
The paper tackles the problem of learning isometry-invariant representations for images undergoing geometric transformations like rotation and translation, resulting in a novel graph-based network that achieves high performance on transformed test images compared to classical architectures.
Learning transformation invariant representations of visual data is an important problem in computer vision. Deep convolutional networks have demonstrated remarkable results for image and video classification tasks. However, they have achieved only limited success in the classification of images that undergo geometric transformations. In this work we present a novel Transformation Invariant Graph-based Network (TIGraNet), which learns graph-based features that are inherently invariant to isometric transformations such as rotation and translation of input images. In particular, images are represented as signals on graphs, which permits to replace classical convolution and pooling layers in deep networks with graph spectral convolution and dynamic graph pooling layers that together contribute to invariance to isometric transformations. Our experiments show high performance on rotated and translated images from the test set compared to classical architectures that are very sensitive to transformations in the data. The inherent invariance properties of our framework provide key advantages, such as increased resiliency to data variability and sustained performance with limited training sets.