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 = {Bronstein, MM and Bronstein, AM and Michel, F and Paragios, N},
doi = {10.1109/CVPR.2010.5539928},
pages = {3594--3601},
title = {Data fusion through cross-modality metric learning using similarity-sensitive hashing},
url = {},
year = {2010}

RIS format (EndNote, RefMan)

AB - Visual understanding is often based on measuring similarity between observations. Learning similarities specific to a certain perception task from a set of examples has been shown advantageous in various computer vision and pattern recognition problems. In many important applications, the data that one needs to compare come from different representations or modalities, and the similarity between such data operates on objects that may have different and often incommensurable structure and dimensionality. In this paper, we propose a framework for supervised similarity learning based on embedding the input data from two arbitrary spaces into the Hamming space. The mapping is expressed as a binary classification problem with positive and negative examples, and can be efficiently learned using boosting algorithms. The utility and efficiency of such a generic approach is demonstrated on several challenging applications including cross-representation shape retrieval and alignment of multi-modal medical images. ©2010 IEEE.
AU - Bronstein,MM
AU - Bronstein,AM
AU - Michel,F
AU - Paragios,N
DO - 10.1109/CVPR.2010.5539928
EP - 3601
PY - 2010///
SN - 1063-6919
SP - 3594
TI - Data fusion through cross-modality metric learning using similarity-sensitive hashing
UR -
ER -