The Divergence and Bhattacharyya Distance Measures in Signal Selection

By Thomas Kailath. 1967

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Citations in the corpus

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting (2019, background): “where ρ(p,q) = ∑j pjqj is the Bhattacharya coefficient [22] measuring the overlap between discrete distributions – withpj ,qj being elements of probability vectors p,q respectively.”
Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization (2008, background): “signal selection (Kailath, 1967), decentralized detection (Nguyen et al.”
Remote sensing techniques for mangrove mapping (2006, methodology): “The analysts then have to select the best combination of bands by using separability indices as selection criteria (Kailath, 1967; Swain and King, 1973; Swain and Davis, 1978; Mausel et al., 1990; Bruzzone and Serpico, 2000).”
Posterior Regularization for Structured Latent Variable Models (2010, methodology): “The Bhattacharyya distance is a very natural, symmetric measure of difference between distributions which has been used in many signal detection applications (Kailath, 1967).”
Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval (2021, background): “Bhattacharyya distance [40] for color covariance-based features, and Euclidean distance for texture features.”
Real-time tracking of non-rigid objects using mean shift (2000, background): “can be proved [19] that, there exists a set of priors for which the error probability for the set is less than the error probability for the set .”
An adaptive color-based particle filter (2003, methodology): “A popular measure between two distributions pðuÞ and qðuÞ is the Bhattacharyya coefficient [1,17]”
Nonparametric estimation of the likelihood ratio and divergence functionals (2007, methodology): “Examples include signal selection [12] and decentralized detection [15], wheref -divergences are used to solve experimental design problems.”
Symmetrizing the Kullback-Leibler Distance (2001, background): “The Kullback-Leibler distance is not symmetric, and the Chernoff and Bhattacharyya distances do not satisfy the triangle inequality [14].”
Adaptive Target Birth Intensity for PHD and CPHD Filters (2012, methodology): “The similarity between the ground truth sk(y) of (53) and the PHD/CPHD filter estimate ŝkjk(y) of (51), is measured using the Bhattacharyya distance [24]:”
$f$ -Divergence Inequalities (2015, background): “Introduced in [8], the Bhattacharyya distance was popularized in the engineering literature in [55].”
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow (2007, background): “Such a natural generalization is known as the average Bhattacharyya coefﬁcient and it is given by [21] (35)”
Current status and key issues in image steganography: A survey (2014, background): “Security will be improved if selected cover image is with smaller ρ [15] [16] [17].”
Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries (2004, background): “For example, the Bhattacharyya coefficient [20], p, is often cited as a measure of the similarity between two Gaussian classes, Xi and X2, each defined by means and covariances, (pi, £1) and (/i2, £2), respectively.”