text: Abstract Image patch matching, which is the process of identifying corresponding patches across images, has been used as a subroutine for many computer vision and image processing tasks. State -of-the-art patch matching techniques take image patches as input to a convolutional neural network to extract the patch features and evaluate their similarity. Our aim in this paper is to improve on the state of the art patch matching techniques by observing the fact that a sparse-overcomplete representation of an image posses statistical properties of natural visual scenes which can be exploited for patch matching. We propose a new paradigm which encodes image patch details by encoding the patch and subsequently using this sparse representation as input to a neural network to compare the patches. As sparse coding is based on a generative model of natural image patches, it can represent the patch in terms of the fundamental visual components from which it has been composed of, leading to similar sparse codes for patches which are built from similar components. Once the sparse coded features are extracted, we employ a fully-connected neural network, which captures the non-linear relationships between features, for comparison. We have evaluated our approach using the Liberty and Notredame subsets of the popular UBC patch dataset and set a new benchmark outperforming all state-of-the-art patch matching techniques for these datasets.
  @cite_9: In this paper, we explore methods for learning local image descriptors from training data. We describe a set of building blocks for constructing descriptors which can be combined together and jointly optimized so as to minimize the error of a nearest-neighbor classifier. We consider both linear and nonlinear transforms with dimensionality reduction, and make use of discriminant learning techniques such as Linear Discriminant Analysis (LDA) and Powell minimization to solve for the parameters. Using these techniques, we obtain descriptors that exceed state-of-the-art performance with low dimensionality. In addition to new experiments and recommendations for descriptor learning, we are also making available a new and realistic ground truth data set based on multiview stereo data.
  @cite_7: In this paper we show how to learn directly from image data (i.e., without resorting to manually-designed features) a general similarity function for comparing image patches, which is a task of fundamental importance for many computer vision problems. 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.
summary: In the approach suggested by Zagoruyko and Komodakis @cite_7 they have evaluated three main neural network architectures for the patch matching. The architectures they have suggested are 1) Siamese, 2) Pseudo-siamese and 3) 2-channel. The siamese and pseudo-siamese architectures contain two branches and as the input each branch takes one of the two patches to be compared. The output of these two branches are analogous to the feature descriptors in the traditional approaches and the branches are merged at the top to make the comparison. In contrast to the siamese architecture where the weights of the two branches are shared in the pseudo siamese architecture the weights are uncoupled. In 2-channel architecture two patches are considered as a 2 channel image where there is no explicit separation on feature descriptor generation and matching. The evaluations on these architectures have been carried out on UBC dataset @cite_9 . Their evaluations conclude that when the convolution layer was divided into small kernels of size @math , 2 channel architecture performs the best.