Fusion of Image Segmentations under Markov Random Fields

Date

2014-08-28

Author

Karadag, Ozge Oztimur
Yarman Vural, Fatoş Tunay

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this study, a fast and efficient consensus segmentation method is proposed which fuses a set of baseline segmentation maps under an unsupervised Markov Random Fields (MRF) framework. The degree of consensus among the segmentation maps are estimated as the relative frequency of co-occurrences among the adjacent segments. Then, these relative frequencies are used to construct the energy function of an unsupervised MRF model. It is well-known that MRF framework is commonly used for formulating the spatial relationships among the super-pixels, under the Potts model. In this study, the Potts model is reorganized to represent the degree of consensus among the spatially adjacent segments (super-pixels). The proposed segmentation fusion method, called, Boosted-MRF, is tested in various experimental setups, and its performance is compared to the state of the art segmentation methods and satisfactory results are obtained.

Subject Keywords

Computer Science, Artificial Intelligence, Computer Science, Theory & Methods, Engineering, Electrical & Electronic

URI

https://hdl.handle.net/11511/39641

DOI

https://doi.org/10.1109/icpr.2014.170

Conference Name

22nd International Conference on Pattern Recognition (ICPR)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

O. O. Karadag and F. T. Yarman Vural, “Fusion of Image Segmentations under Markov Random Fields,” presented at the 22nd International Conference on Pattern Recognition (ICPR), Swedish Soc Automated Image Anal, Stockholm, SWEDEN, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39641.