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I have asked this question on stack overflow and has been answered : http://stackoverflow.com/questions/14230377/opencv-output-of-the-predict-function-of-expectation-maximization

Here is the copy of the answer given in stackoverflow:

As I understand you have two separate GMMs for the foreground and background part of the image.The total probability of a sample pixel 'x' in the test image when evaluated in the foreground GMM is
P_fg(x) = sum_over_j_1_to_k ( Wj_fg * Pj_fg( x ))
where 
k = number of clusters in foreground GMM
x = test sample
Pj_fg(x) = probability that sample x is in j-th  cluster according to the foreground GMM
Wj_fg = weight of the j-th cluster in foreground GMM
also, sum of all weights should be 1 for each GMM.`
We can do a similar calculation for the background GMM.

From looking at the EM code in opencv, it looks like the first part of the 2 values that EM returns is the log likelihood. For the foreground GMM this is
 log(P_fg(x_i))
I implemented your algorithm and for each pixel in the test image, I compared the log-likelihoods returned for each of the two GMM-s and classified the pixel with the GMM with higher value. I got decent results.

In that respect, yes this value is an indication of the pixel to be belonging to the entire GMM.

2) In my implementation of your problem, I always got the log likelihoods of all GMMS of all test-sample pixels under 0.