Deep Spectral Graph Matching

In this work, we present a Deep Learning based approach for visual correspondence estimation, by deriving a Deep spectral graph matching network. We formulate the state-of-the-art unsupervised Spectral Graph Matching (SGM) approach, as part of an end-to-end supervised deep learning network. Thus allowing to utilize backpropagation to learn optimal image features, as well as algorithm parameters. For that, we present a transformation layer that converts the learned image feature, within a pair of images, to an affinity matrix used to solve the matching problem via a new metric loss function. The proposed scheme is shown to compare favorably with contemporary state-of-the-art matching schemes when applied to annotated data obtained from the PASCAL, ILSVRC, KITTI and CUB-2011 datasets.

Joint work with Yoav Liberman.

Yosi Keller is an Associate Professor at the Faculty of Engineering in Bar Ilan University, and a co-founder of the Deep Learning  Lab at the faculty. Professor Keller is a graduate of the Electric Engineering Faculty in the Technion IOT (BSc) and the Faculty of Engineering in Tel Aviv University (MSc and PhD). During 2003-2006 he served as a Gibbs Assistant Professor at Yale University and joined The Engineering Faculty in BIU in 2007.  His research group studies Data Science in general and Deep Learning in particular with applications to biometrics, computer vision and graph algorithms.