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The 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...
The design of feature spaces for local image descriptors is an important research subject in computer vision due to its applicability in several problems, such as visual classification and image matching. In order to be useful, these descriptors have to present a good trade off between discriminating power and robustness to typical image deformations. The feature spaces of the most useful local descriptors...
We introduce a new method that characterizes quantitatively local image descriptors in terms of their distinctiveness and robustness to geometric transformations and brightness deformations. The quantitative characterization of these properties is important for recognition systems based on local descriptors because it allows for the implementation of a classifier that selects descriptors based on...
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