Learning moment-based fast local binary descriptor
Résumé
Recently, binary descriptors have attracted significant attention due to their speed and low memory
consumption; however, using intensity differences to calculate the binary descriptive vector is not efficient
enough. We propose an approach to binary description called POLAR_MOBIL, in which we perform binary
tests between geometrical and statistical information using moments in the patch instead of the classical intensity
binary test. In addition, we introduce a learning technique used to select an optimized set of binary tests with
low correlation and high variance. This approach offers high distinctiveness against affine transformations and
appearance changes. An extensive evaluation on well-known benchmark datasets reveals the robustness
and the effectiveness of the proposed descriptor, as well as its good performance in terms of low computation
complexity when compared with state-of-the-art real-time local descriptors.
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