Imperial College London


Faculty of EngineeringDepartment of Computing

Chair in Machine Learning and Pattern Recognition



m.bronstein Website




569Huxley BuildingSouth Kensington Campus






BibTex format

author = {Michel, F and Bronstein, M and Bronstein, A and Paragios, N},
doi = {10.1109/ISBI.2011.5872619},
pages = {1209--1214},
title = {Boosted metric learning for 3D multi-modal deformable registration},
url = {},
year = {2011}

RIS format (EndNote, RefMan)

AB - Defining a suitable metric is one of the biggest challenges in deformable image fusion from different modalities. In this paper, we propose a novel approach for multi-modal metric learning in the deformable registration framework that consists of embedding data from both modalities into a common metric space whose metric is used to parametrize the similarity. Specifically, we use image representation in the Fourier/Gabor space which introduces invariance to the local pose parameters, and the Hamming metric as the target embedding space, which allows constructing the embedding using boosted learning algorithms. The resulting metric is incorporated into a discrete optimization framework. Very promising results demonstrate the potential of the proposed method. © 2011 IEEE.
AU - Michel,F
AU - Bronstein,M
AU - Bronstein,A
AU - Paragios,N
DO - 10.1109/ISBI.2011.5872619
EP - 1214
PY - 2011///
SN - 1945-7928
SP - 1209
TI - Boosted metric learning for 3D multi-modal deformable registration
UR -
ER -