An efficient image similarity measure based on approximations of KL-divergence between two gaussian mixtures

By Jacob Goldberger, Shiri Gordon, Hayit Greenspan. Proceedings Ninth IEEE International Conference on Computer Vision, 2003

508 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/caf325643226cb3859059ac5aabe7c1f14b15e64

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In Computer Vision and Pattern Recognition

Citations in the corpus

Distance based similarity models for content based multimedia retrieval (2013, methodology): “One approximation of the Kullback-Leibler Divergence between Gaussian mixture models is the matching-based Goldberger approximation [Goldberger et al., 2003].”
Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models (2007, methodology): “al, [6], suggest a similar approximate formula to estimate D(f 19).”
On entropy approximation for Gaussian mixture random vectors (2008, background): “This is the idea the entropy approximation proposed in [9] is based on.”
DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting (2022, methodology): “There are many methods for measuring the di ff erence in probability distributions, such as Kullback-Leibler (KL) divergence (Goldberger et al., 2003), Hellinger Distance (Kailath, 1967), and Total Variation Distance (Devroye et al., 2018).”
Continual Variational Autoencoder Learning via Online Cooperative Memorization (2022, background): “Directly evaluating each Si(j, u) in the high-dimensional data space is intractable since it would require overloaded computations [17] and auxiliary training [7,37].”
Divergence Estimation for Multidimensional Densities Via $k$-Nearest-Neighbor Distances (2009, methodology): “…divergence has been used for detecting changes or testing stationarity in Internet measurements [6], [21], and for classification purposes in speech recognition [30], image registration [15], [29], text/multimedia clustering [11], and in a general classification framework called KLBoosting [26].”
A robust algorithm for point set registration using mixture of Gaussians (2005, methodology): “For instance, an approximation of KL between two mixtures densities was suggested in [ 14 ] as follows :”
Comparison of approximation methods to Kullback–Leibler divergence between Gaussian mixture models for satellite image retrieval (2016, methodology): “The unscented transformation (Julier and Uhlmann 1997a; Goldberger et al. 2003) is a method to estimate the expectation E f ( x ) [ h ( x )] of a function h ( x ) with respect to a Gaussian probability density function f ( x ) = N ( X ; µ , Σ ).”