Image Steganography with Symmetric Embedding using Gaussian Markov Random Field Model

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Wenkang Su ◽  
Jiangqun Ni ◽  
Xianglei Hu ◽  
Jessica Fridrich
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Field Model ◽  
Image Steganography ◽  
Random Field Model ◽  
Markov Random Field Model ◽  
Gaussian Markov Random Field ◽  
Markov Random

Conditional cyclic Markov random fields

1996 ◽  
Vol 28 (1) ◽  
pp. 1-12 ◽  
Author(s):  
John T. Kent ◽  
Kanti V. Mardia ◽  
Alistair N. Walder
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Random Fields ◽  
Markov Random Fields ◽  
Field Model ◽  
Random Field Model ◽  
Markov Random Field Model ◽  
Gaussian Markov Random Field ◽  
Markov Random ◽  
Limiting Behaviour

Grenander et al. (1991) proposed a conditional cyclic Gaussian Markov random field model for the edges of a closed outline in the plane. In this paper the model is recast as an improper cyclic Gaussian Markov random field for the vertices. The limiting behaviour of this model when the vertices become closely spaced is also described and in particular its relationship with the theory of ‘snakes' (Kass et al. 1987) is established. Applications are given in Grenander et al. (1991), Mardia et al. (1991) and Kent et al. (1992).

Conditional cyclic Markov random fields

1996 ◽  
Vol 28 (01) ◽  
pp. 1-12 ◽  
Author(s):  
John T. Kent ◽  
Kanti V. Mardia ◽  
Alistair N. Walder
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Random Fields ◽  
Markov Random Fields ◽  
Field Model ◽  
Random Field Model ◽  
Markov Random Field Model ◽  
Gaussian Markov Random Field ◽  
Markov Random ◽  
Limiting Behaviour

Grenander et al. (1991) proposed a conditional cyclic Gaussian Markov random field model for the edges of a closed outline in the plane. In this paper the model is recast as an improper cyclic Gaussian Markov random field for the vertices. The limiting behaviour of this model when the vertices become closely spaced is also described and in particular its relationship with the theory of ‘snakes' (Kass et al. 1987) is established. Applications are given in Grenander et al. (1991), Mardia et al. (1991) and Kent et al. (1992).

scholarly journals Color-Texture-Based Image Retrieval System Using Gaussian Markov Random Field Model

2009 ◽  
Vol 2009 ◽  
pp. 1-17 ◽  
Author(s):  
Meng-Hsiun Tsai ◽  
Yung-Kuan Chan ◽  
Jiun-Shiang Wang ◽  
Shu-Wei Guo ◽  
Jiunn-Lin Wu
Keyword(s):  
Image Retrieval ◽  
Random Field ◽  
Markov Random Field ◽  
Field Model ◽  
Query Image ◽  
Retrieval Method ◽  
Random Field Model ◽  
Markov Random Field Model ◽  
Gaussian Markov Random Field ◽  
Markov Random

The techniques of -means algorithm and Gaussian Markov random field model are integrated to provide a Gaussian Markov random field model (GMRFM) feature which can describe the texture information of different pixel colors in an image. Based on this feature, an image retrieval method is also provided to seek the database images most similar to a given query image. In this paper, a genetic-based parameter detector is presented to decide the fittest parameters used by the proposed image retrieval method, as well. The experimental results manifested that the image retrieval method is insensitive to the rotation, translation, distortion, noise, scale, hue, light, and contrast variations, especially distortion, hue, and contrast variations.

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