Nonparametric Bayesian Extraction of Object Configurations in Massive Data

This study presents an unsupervised method for detection of configurations of objects based on a point process in a nonparametric Bayesian framework. This is of interest as the model presented here has a number of parameters that increases with the number of objects detected. The marked point proces...

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Bibliographic Details
Published inIEEE transactions on signal processing Vol. 63; no. 8; pp. 1911 - 1924
Main Authors Meillier, Celine, Chatelain, Florent, Michel, Olivier, Ayasso, Hacheme
Format Journal Article
LanguageEnglish
Published IEEE 15.04.2015
Institute of Electrical and Electronics Engineers
Subjects
Bayes methods
Computer Science
Data models
Detection
Equations
hyperspectral
Hyperspectral imaging
marked point process
Markov chain Monte Carlo method
Microscopy
Object detection
Signal and Image Processing
Signal processing algorithms
marked point process
Markov chain Monte Carlo method
Detection
hyperspectral
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Summary:This study presents an unsupervised method for detection of configurations of objects based on a point process in a nonparametric Bayesian framework. This is of interest as the model presented here has a number of parameters that increases with the number of objects detected. The marked point process yields a natural sparse representation of the object configuration, even in massive data fields. However, Bayesian methods can lead to the evaluation of some densities that raise computational issues, due to the huge number of detected objects. We have developed an iterative update of these densities when changes in the object configurations are made, which allows the computational cost to be reduced. The performance of the proposed algorithm is illustrated on synthetic data and very challenging quasi-real hyperspectral data for young galaxy detection.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2015.2403268