Fusion of Image Segmentations under Markov Random Fields

Karadag, Ozge Oztimur
Yarman Vural, Fatoş Tunay
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.


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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.