Solved – Machine learning to find an optimal set of parameters for a segmentation algorithm

fittinghyperparameterimage processingmachine learningoptimization

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.

Best Answer

You can make more intelligent search strategies for finding your ideal parameters. For example you can use the Sequential Model-Based Optimization algorithm (, 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).