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Proximal Splitting Networks for Image Restoration Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Spatially-Coherent Segmentation Using Hierarchical Gaussian Mixture Reduction Based on Cauchy-Schwarz Divergence

Authors : Adama Nouboukpo, Mohand Said Allili

Published in: Image Analysis and Recognition

Publisher: Springer International Publishing

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Abstract

Gaussian mixture models (GMM) are widely used for image segmentation. The bigger the number in the mixture, the higher will be the data likelihood. Unfortunately, too many GMM components leads to model overfitting and poor segmentation. Thus, there has been a growing interest in GMM reduction algorithms that rely on component fusion while preserving the structure of data. In this work, we present an algorithm based on a closed-form Cauchy-Schwarz divergence for GMM reduction. Contrarily to previous GMM reduction techniques which a single GMM, our approach can lead to multiple small GMMs describing more accurately the structure of the data. Experiments on image foreground segmentation demonstrate the effectiveness of our proposed model compared to state-of-art methods.

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Metadata
Title
Spatially-Coherent Segmentation Using Hierarchical Gaussian Mixture Reduction Based on Cauchy-Schwarz Divergence
Authors
Adama Nouboukpo
Mohand Said Allili
Copyright Year
2019
Book
Image Analysis and Recognition

Print ISBN: 978-3-030-27201-2

Electronic ISBN: 978-3-030-27202-9

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-27202-9_35

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