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Summary: To be invariant, or not to be invariant: that is the query formulated on this work about local descriptors. A limitation of current feature descriptors is the commerce-off between generalization and discriminative energy: more invariance means less informative descriptors. We suggest to overcome this limitation with a disentanglement of invariance in local descriptors and with an internet choice of probably the most acceptable invariance given the context. Our framework consists in a joint studying of a number of local descriptors with different levels of invariance and of meta descriptors encoding the regional variations of an image. The similarity of those meta descriptors throughout pictures is used to pick out the fitting invariance when matching the local descriptors. Our strategy, named Local Invariance Choice at Runtime for Descriptors (LISRD), permits descriptors to adapt to antagonistic modifications in pictures, whereas remaining discriminative when invariance will not be required. We demonstrate that our technique can increase the efficiency of current descriptors and outperforms state-of-the-art descriptors in several matching tasks, when evaluated on difficult datasets with day-night time illumination as well as viewpoint changes.

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