Evidential based Expectation Maximization for Image Segmentation

DOI

10.2991/mecae-17.2017.10How to use a DOI?

Keywords

Expectation Maximization, Spatial Contextual Information, Dempster-Shafer's Theory (DST) of Evidence, Maximum A Posteriori (MAP).

Abstract

In statistics, expectation-maximization (EM) algorithm is an iterative method which finds the maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models depending on unobserved latent variables. In image segmentation, EM is widely used to determine the unknown parameters of different visual objects existing in an image. However, the main drawback of the EM method is that it does not consider spatial contextual information, which may entail rather noisy segmentation results. To remedy this, we develop an evidence theory based EM method (EEM) which incorporates spatial contextual information in EM by iteratively fusing the belief assignments of neighboring pixels to the central pixel. A simulated image set is used to evaluate the proposed method. Experimental results show that the new evidential method can achieve relative high accuracy.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yin Chen
AU  - Siyu Hu
AU  - Armin Cremers
PY  - 2017/03
DA  - 2017/03
TI  - Evidential based Expectation Maximization for Image Segmentation
BT  - Proceedings of the 2017 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2017)
PB  - Atlantis Press
SP  - 56
EP  - 61
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-17.2017.10
DO  - 10.2991/mecae-17.2017.10
ID  - Chen2017/03
ER  -