Carneiro, G.2013-04-042013-04-042010ICPR 2010: 20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August 2010: pp.3356-335997814244754211051-4651http://hdl.handle.net/2440/76410The use of an ensemble of feature spaces trained with distance metric learning methods has been empirically shown to be useful for the task of automatically designing local image descriptors. In this paper, we present a quantitative analysis which shows that in general, nonlinear distance metric learning methods provide better results than linear methods for automatically designing local image descriptors. In addition, we show that the learned feature spaces present better results than state of- the-art hand designed features in benchmark quantitative comparisons. We discuss the results and suggest relevant problems for further investigation.en© 2010 IEEEConference paper002011436010.1109/ICPR.2010.8192-s2.0-7814948808227018Carneiro, G. [0000-0002-5571-6220]