Is it likely that many pixels in the image will have identical distributions? It seems that "apple" pixels near the edge of the apple would have a different probability distribution than those near the center. All the ML-based segmentation systems I've seen either predict a single output class for a pixel (such as a binary classifier), or they produce probability vector
It seems like a bit of a halfway solution to define a small set of distributions that the image uses as an index, then transmit that set with every result. I feel that these two options would work based on use case:
1. The image is segmented in some small finite set of output classes, which do not have probability distributions that vary in space/time: use an Image message where the lookup value of the pixel is the output class. If desired, static probability distributions for each class can be communicated in a one-time fashion, such as via a single CategoryDistribution[] message, or via the parameter server
2. The output segmentation includes varying probability distributions that are calculated per-pixel or per-small region: use a CategoryDistribution of length the size of the image, where each pixel has its own unique distribution that may change every frame.
Let me know if I missed something! If you have some code available for a use case, that's really helpful. I'm currently in the process of writing example classifiers to use the Classification/Detection messages and finding it a useful exercise.
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