Abstract
In this paper, we introduce BoSP (Bonn Salient Points), a method comprising a pair of a keypoint detector and descriptor in image data that are deeply geared to one another. Our detector identifies points of interest to be local maxima of appearance contrast to their surroundings in a statistical manner. This criterion admits a selection of particularly repeatable, but diverse looking keypoints. Besides, those textural statistics collected around a keypoint location directly serve as its descriptor. An important component in this framework is how to gather and represent local statistics. Regarding this, we further improved the efficient ML-estimation procedure for multivariate normal distributions previously introduced by Klein and Frintrop [6]. This Gaussian representation of feature statistics enables a quickly computable, closed-form solution of the \(\mathcal{W}_2\)-distance, which we utilize as a measure of appearance contrast. Evaluations were conducted comparing several recent detector/descriptor pairs on a well-recognized, publicly available dataset.
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Klein, D.A., Cremers, A.B. (2013). Discriminable Points That Stick Out of Their Environment. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_41
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DOI: https://doi.org/10.1007/978-3-642-40602-7_41
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