The quantitative characterization of the distinctiveness and robustness of local image descriptors

Abstract

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 their distinctiveness and robustness properties. This classification results in: (a) recognition time reduction due to a smaller number of descriptors present in the test image and in the database of model descriptors; (b) improvement of the recognition accuracy since only the most reliable descriptors for the recognition task are kept in the model and test images; and (c) better scalability given the smaller number of descriptors per model. Moreover, the quantitative characterization of distinctiveness and robustness of local descriptors provides a more accurate formulation of the recognition process, which has the potential to improve the recognition accuracy. We show how to train a multi-layer perceptron that quickly classifies robust and distinctive local image descriptors. A regressor is also trained to provide quantitative models for each descriptor. Experimental results show that the use of these trained models not only improves the performance of our recognition system, but it also reduces significantly the computation time for the recognition process.