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Published Articles >> Table of Contents >> Abstract
18th International Conference on Pattern Recognition (ICPR'06) Volume 2
pp. 365-368
New MRF Parameter Estimation Technique for Texture Image Segmentation using Hierarchical GMRF Model Based on Random Spatial Interaction and Mean Field Theory
Dong Hwan Kim, Seoul Nat’l Univ., Korea
Il Dong Yun, Hankuk Univ. of F.S., Korea
Sang Uk Lee, Seoul Nat’l Univ., Korea
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.856
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| Abstract |
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This paper presents a new Markov Random Field (MRF)
parameter estimation technique using hierarchical MRF
model based on the random spatial interaction (RSI) and
the mean field theory for the textured image segmentation.
By considering spatial interaction of the MRF as random
fields, the fluctuation of the spatial interaction that occurs
in the conventional MRF model can be efficiently alleviated.
Also, by assuming randomness of the spatial interaction as
the MRF model, it allows us to obtain more robust information
for segmentation during the feature extraction. The
Gaussian MRF model is applied to the proposed hierarchical
MRF scheme, and the expectation of the RSI is uniquely
obtained by simple linear equation without using a window
based on the mean field theory. Experimental results on
synthetic and real world images show that the proposed algorithm
provides good feature extraction and segmentation.
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 Additional Information
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Citation:
Dong Hwan Kim, Il Dong Yun, Sang Uk Lee,
"New MRF Parameter Estimation Technique for Texture Image Segmentation using Hierarchical GMRF Model Based on Random Spatial Interaction and Mean Field Theory,"
icpr,
pp. 365-368,
18th International Conference on Pattern Recognition (ICPR'06) Volume 2,
2006
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