A test image to be segmented is represented in the same way as the training image by a 1-D array X. In the case oftest image, a 1-D array Y = [y\, y2,..., yN) is the estimate of the segmentation. We can now adopt one of the two strategies for test image segmentation.
1. Supervised segmentation with GMM: Using the ML estimate of the parameter values obtained from the training images, a segmentation of the test images is performed. This is achieved by substituting the learnt model parameters 0 from training when performing testing. The image is segmented by setting the class label estimate yn of pixel xn as the one with the maximum estimate of the component-conditional probability.
2. Unsupervised segmentation with GMM: This alternative approach assumes no a priori knowledge except for the number of classes in the image corresponding to the number of components in the GMM, L = M. Therefore, the weight Ymn = 1 indicates that all samples are considered as being generated from this distribution. Using the GMM-EM algorithm, an ML estimate of the parameter values is found. The segmentation can then be estimated using the GMM by extracting the component-conditional probabilities using the Bayes rule.
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