Compensating for Large In-Plane Rotations in Natural Images
This addresses a specific challenge in computer vision for applications requiring rotation invariance, but it is incremental as it builds on existing concepts with a new method.
The paper tackled the problem of achieving invariance to large in-plane rotations in natural images, which is underexplored compared to small rotations, by directly compensating for rotations using a method inspired by mental rotation, resulting in a task-agnostic approach that improves robustness in image retrieval.
Rotation invariance has been studied in the computer vision community primarily in the context of small in-plane rotations. This is usually achieved by building invariant image features. However, the problem of achieving invariance for large rotation angles remains largely unexplored. In this work, we tackle this problem by directly compensating for large rotations, as opposed to building invariant features. This is inspired by the neuro-scientific concept of mental rotation, which humans use to compare pairs of rotated objects. Our contributions here are three-fold. First, we train a Convolutional Neural Network (CNN) to detect image rotations. We find that generic CNN architectures are not suitable for this purpose. To this end, we introduce a convolutional template layer, which learns representations for canonical 'unrotated' images. Second, we use Bayesian Optimization to quickly sift through a large number of candidate images to find the canonical 'unrotated' image. Third, we use this method to achieve robustness to large angles in an image retrieval scenario. Our method is task-agnostic, and can be used as a pre-processing step in any computer vision system.