Dear all,
I am trying to implement this paper (Skeleton-based shape classification using path similarity) in Processing but have difficulties understanding formulas 5 and 7 on page 7.
The goal is to find a shape (named “class”) whose topology is the closest to a query shape.
In this example, the probability that the query shape belongs to class C should be the highest
More specifically I am trying to calculate the “posterior probability” that a query shape belongs to a given class (formula 7)
What I understand: This “posterior probability” is based on “path similarity”, i.e how far/close the skeleton paths of the compared shapes (“query” and “class”) are.
-
a skeleton path (
sp) is the route along the edges of the skeleton that starts at one vertex of the polygonal shape and ends at another one. -
the distance is the difference between the respective radii of the
mequidistant maximum disks along 2 different skeleton paths
What I DO NOT understand: Path comparison.
Do I need to compare each path of the query shape against each path of a class shape (nested for loop) ?
If not, what paths do I need to compare ?
Again, I believe understanding formula 7 is key to get this right
Here a snippet in Python mode showcasing the example displayed on the first picture (query shape vs A/B/C classes):