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Using machine learning to find an optimal set of parameters for a given segmentation algorithm.

In the "classical" case of machine learning, in the training phase, the data set is constant and the model fit -- produces a weight vector that maps the data set to the tag of each image label.

Now let's assume a given segmentation problem X, which is done using a given classic segmentation algorithm Y (classic, not Deep-Learning). The goal is to find an optimal parameter set for the Y algorithm under the set of ground truth segmentation. (Motivation: every segmentation algo. Have tuned, parameters, we want to learn them not to fine tune them)

I think about the two approaches of making the this:

  1. Offline - Extraction of General properties - let's say Haralick texture features and try to fit a model connecting between the parameters of the segmentation algorithm Y to the Haralick texture features.
  2. Online - Select random parameters for the Segmentation Y algorithm. Perform a segmentation for those parameters and a specific delta. Calculate the error and then updated the parameters accordingly.

Any example/reference(paper) for the "online" approach would be welcome.

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  • $\begingroup$ Quite a few algorithms have an "online" variant... Is there a particular that interests you? $\endgroup$ – usεr11852 May 3 '17 at 23:14
  • $\begingroup$ One or two examples of such algorithm for image segmentation with references/papers would be great $\endgroup$ – Dov May 4 '17 at 7:48
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You can make more intelligent search strategies for finding your ideal parameters. For example you can use the Sequential Model-Based Optimization algorithm (https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf), for identifying promising points in your hyperparameter space. That explores regions of the hyperparameter space, that are unexplored and promise good results (based on your previous experiments).

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