Held C, Nattkemper T, Palmisano R, Wittenberg T. Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis. J Pathol Inform 2013;4:5. DOI: 10.4103/2153-3539.109831
I once heard Larry Wasserman claim that all problems in statistics are solved, except one, how to set λ. By which he meant (or I understood or I remember; in fact, he may not even have claimed this and I am just assigning a nice quip to a famous name) that we have methods that work very well on most settings, but they tend to come with parameters and adjusting these parameters (often called λ₁, λ₂… in statistics) is what is pretty hard.
In traditional image processing, parameters abound too. Thresholds and weights are abundant in the published literature. Often, tuning them to a specific dataset is an unfortunate necessity. It also makes the published results from different authors almost incomparable as they often tune their own algorithms much harder than those of others.
In this paper, the problem of setting the parameters is viewed as an optimization problem using a supervised machine learning approach where the goal is to set parameters that reproduce a gold standard.
The set up is interesting and it’s definitely a good idea to explore this way of thinking. Unfortunately, the paper is very short (just as it’s getting good, it ends). Thus, there aren’t a lot of results, except the observations that local minima can be a problem and that genetic algorithms do pretty well at a high computational cost. For example, there is a short discussion of the human behaviour in parameter tuning and one is hoping for an experimental validation of these speculations (particularly given that the second author is well-known for earlier work on this theme).
I will be looking out for follow-up work from the same authors.