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Comparison of Approximation Methods to Kullback-Leibler Divergence between Gaussian Mixture Models for Satellite Image Retrieval

Cui, Shiyong (2016) Comparison of Approximation Methods to Kullback-Leibler Divergence between Gaussian Mixture Models for Satellite Image Retrieval. IEEE Geoscience and Remote Sensing Letters, 7 (7), pp. 651-660. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1080/2150704X.2016.1177241 ISSN 1545-598X

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Official URL: http://dx.doi.org/10.1080/2150704X.2016.1177241

Abstract

As a probabilistic distance between two probability density functions, Kullback-Leibler divergence is widely used in many applications, such as image retrieval and change detection. Unfortunately, for some models, e.g., Gaussian Mixture Models (GMMs), Kullback-Leibler divergence is not analytically tractable. One has to resort to approximation methods. A number of methods have been proposed to address this issue. In this letter, we compare seven methods, namely Monte Carlo method, matched bound approximation, product of Gaussians, variational method, unscented transformation, Gaussian approximation, and min-Gaussian approximation, for approximating the Kullback-Leibler divergence between two Gaussian mixture models for satellite image retrieval. Two experiments using two public datasets have been performed. The comparison is carried out in terms of retrieval accuracy and computational time.

Item URL in elib:https://elib.dlr.de/103789/
Document Type:Article
Title:Comparison of Approximation Methods to Kullback-Leibler Divergence between Gaussian Mixture Models for Satellite Image Retrieval
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Cui, ShiyongRemote Sensing Technology Institute (IMF)UNSPECIFIED
Date:April 2016
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:7
DOI :10.1080/2150704X.2016.1177241
Page Range:pp. 651-660
Editors:
EditorsEmail
Foody, GilesUniversity of Nottingham, UK
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1545-598X
Status:Published
Keywords:Gaussian Mixture Models (GMMs), Kullback-Leibler Divergence, Image retrieval
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Cui, Shiyong
Deposited On:08 Apr 2016 15:04
Last Modified:31 Jul 2019 20:01

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