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Supervised Image Segmentation Based on Tree-Structured MRF Model in Wavelet Domain
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DOI：10.1109/LGRS.2009.2026719.png)
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Abstract
En 中文
In the tree-structured Markov random field (TSMRF) model, a sequence of MRFs was hierarchically defined on the single spatial resolution in the format of a tree structure which might suffer from the deficiency of modeling the nonstationary property of a given image. In order to overcome such a problem and motivated by nonredundant directional selectivity and highly discriminative nature of the wavelet representation, we attempt to introduce the TS-MRF model into the wavelet domain and propose a new image modeling method-WTS-MRF, in which each MRF is defined over a multiresolution subset of the lattice sites corresponding to the wavelet decomposition. Based on WTS-MRF, a supervised image segmentation algorithm is carried out, and experiment on a remotely sensed image proves the better performance than the supervised segmentation algorithm based on the TS-MRF model.
Keywords:
Image segmentation
tree-structured Markov random field (TS-MRF)
TS-MRF model in wavelet domain (WTS-MRF)
wavelet transformation
Journal
IF:
16.4
Papers:
1.0W
Citations:
5.1K