Graph-cut and pairwise MRF
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I try to use graph-cut method but I got some trouble with it. I want to segment an image into foreground and background. For each pixel $x$, I got the probability that it belongs to the background $p(x|phi_b)$ and to the foreground $p(x|phi_f)$ by kernel density estimation (KDE). Here is the log-posterior :
$$ log p(mathcal{L}|x) = left( sum^N_{i=1} sum^N_{j=1} lambda(l_il_j + (1-l_i)(1-l_j)) right) + sum^N_{i=1} logleft( frac{p(x_i|phi_f)}{p(x_i|phi_b)} right) l_i $$
where $mathcal{L} = [l_1...L_N]$, $N$ is the number of pixel in the image.
If I understand, the first term is the pairwise cost, for smoothness and determine the weight on edges between a pixel and its neighbors. According to the equation, I put lambda as a weight on each edges.
The second term is the unary cost and determine the weight on edges between a pixel and the source and the sink. Here, the weight between a pixel and the source is the same that between the pixel and the sink ? Am I right ?
Thanks
graph-theory computer-science image-processing computer-vision
asked Dec 11 '18 at 8:44
mnchapelmnchapel
162
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add a comment |
$begingroup$
I try to use graph-cut method but I got some trouble with it. I want to segment an image into foreground and background. For each pixel $x$, I got the probability that it belongs to the background $p(x|phi_b)$ and to the foreground $p(x|phi_f)$ by kernel density estimation (KDE). Here is the log-posterior :
$$ log p(mathcal{L}|x) = left( sum^N_{i=1} sum^N_{j=1} lambda(l_il_j + (1-l_i)(1-l_j)) right) + sum^N_{i=1} logleft( frac{p(x_i|phi_f)}{p(x_i|phi_b)} right) l_i $$
where $mathcal{L} = [l_1...L_N]$, $N$ is the number of pixel in the image.
If I understand, the first term is the pairwise cost, for smoothness and determine the weight on edges between a pixel and its neighbors. According to the equation, I put lambda as a weight on each edges.
The second term is the unary cost and determine the weight on edges between a pixel and the source and the sink. Here, the weight between a pixel and the source is the same that between the pixel and the sink ? Am I right ?
Thanks
graph-theory computer-science image-processing computer-vision
asked Dec 11 '18 at 8:44
mnchapelmnchapel
162
$endgroup$
add a comment |
$begingroup$
I try to use graph-cut method but I got some trouble with it. I want to segment an image into foreground and background. For each pixel $x$, I got the probability that it belongs to the background $p(x|phi_b)$ and to the foreground $p(x|phi_f)$ by kernel density estimation (KDE). Here is the log-posterior :
$$ log p(mathcal{L}|x) = left( sum^N_{i=1} sum^N_{j=1} lambda(l_il_j + (1-l_i)(1-l_j)) right) + sum^N_{i=1} logleft( frac{p(x_i|phi_f)}{p(x_i|phi_b)} right) l_i $$
where $mathcal{L} = [l_1...L_N]$, $N$ is the number of pixel in the image.
If I understand, the first term is the pairwise cost, for smoothness and determine the weight on edges between a pixel and its neighbors. According to the equation, I put lambda as a weight on each edges.
The second term is the unary cost and determine the weight on edges between a pixel and the source and the sink. Here, the weight between a pixel and the source is the same that between the pixel and the sink ? Am I right ?
Thanks
graph-theory computer-science image-processing computer-vision
asked Dec 11 '18 at 8:44
mnchapelmnchapel
162
$endgroup$
I try to use graph-cut method but I got some trouble with it. I want to segment an image into foreground and background. For each pixel $x$, I got the probability that it belongs to the background $p(x|phi_b)$ and to the foreground $p(x|phi_f)$ by kernel density estimation (KDE). Here is the log-posterior :
$$ log p(mathcal{L}|x) = left( sum^N_{i=1} sum^N_{j=1} lambda(l_il_j + (1-l_i)(1-l_j)) right) + sum^N_{i=1} logleft( frac{p(x_i|phi_f)}{p(x_i|phi_b)} right) l_i $$
where $mathcal{L} = [l_1...L_N]$, $N$ is the number of pixel in the image.
If I understand, the first term is the pairwise cost, for smoothness and determine the weight on edges between a pixel and its neighbors. According to the equation, I put lambda as a weight on each edges.
The second term is the unary cost and determine the weight on edges between a pixel and the source and the sink. Here, the weight between a pixel and the source is the same that between the pixel and the sink ? Am I right ?
Thanks
graph-theory computer-science image-processing computer-vision
graph-theory computer-science image-processing computer-vision
asked Dec 11 '18 at 8:44
mnchapelmnchapel
162
asked Dec 11 '18 at 8:44
mnchapelmnchapel
162
edited Dec 11 '18 at 18:08
mnchapel
asked Dec 11 '18 at 8:44
mnchapelmnchapel
162
asked Dec 11 '18 at 8:44
mnchapelmnchapel
162
asked Dec 11 '18 at 8:44
mnchapelmnchapel
162
162
add a comment |
add a comment |
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