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The goal of annotation is to assign a label to data.
The labeling problem is to assign a label from to each of the sites in.
We assign a label,, to each pixel in the current frame by minimizing an energy function.
On the other hand, label algorithms, such as box plots, assign a label (normal or outlier) to each evidence.
The backscattering effects of objects and scenes in HR SAR images are complex, making it difficult to precisely assign a label for each pixel.
For the Metz dataset we used the threshold of (pK_{i} ge 7.6) as suggested in [4] to assign a label of (1), i.e. binding.
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Each node has assigned a label belonging to the group.
The original LBP assigns a label to each pixel.
Each node of the graph is assigned a label (active or non active).
The feature vector is assigned a label that has highest probability for it.
This can be seen as the cost of assigning a label to a node during the inference step.
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