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    1. POOpenCV: Output of the predict function of Expectation Maximization
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    1. USlightalchemist
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      1. VTUpMod
    1. COThank you for the link. But I don't want to build a graph-cut. As I already know the color for my foreground and background, I try to cluster them and to define the affinity to belongs to one and the other GMM. But after that, I don't use a graph-cut. I try to implement the point 2.1 from this paper http://juew.org/projects/SnapCut/snapcut.htm (this is the page project, you can choose the quality of the pdf). I will try to compute the probability using the weight and the probability of each cluster.
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    2. COActually, it can't work using the probability of each cluster and the weight. The sum of the weight is 1 and the sum of probability is 1 too. for example, if I have 2 clusters in my GMM with weights (0.6 and 0.4). Now, I have a pixel very far from the GMM in color space but with probability to belong to each cluster of (0.5 and 0.5), I will have a probability of 0.3. But in reality, I should get something close to 0 as it is very far. That's why I wanted to get the probability to belongs to the whole GMM.
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    3. COHi. My intention of giving the link is to show you how the authors of the graphcut-based image segmentation code used GMM to compute the probability a sample pixel belongs to a foreground/background color model. Basically, what I'm saying is the probability which you obtained from calling the "predict" method of the GMM class is not what you are looking for. The GC segmentation code however does, so check it out. Hope this helps.
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