Image Patch comparison using Convolutional Neural Networks

To encode such a function, we opt for a CNN-based model that is trained to account for a wide variety of changes in image appearance. To that end, we explore and study multiple neural network architectures, which are specifically adapted to this task. We show that such an approach can significantly outperform the state-of-the-art on several problems and benchmark datasets. Comparing patches across images is probably one of the most fundamental tasks in computer vision and image analysis. It is often used as a subroutine that plays an important role in a wide variety of vision tasks. These can range from low-level tasks such as structure from motion, wide baseline matching, building panoramas, and image super-resolution,up to higher-level tasks such as object recognition, image retrieval, and classification of object categories, to mention a few characteristic examples.