which gives a value for how close to polylines are (it normalizes them and does some distance calculations)
the way the vp tree works is you provide a set of data and a metric for distance (in my case, the data was the polylines and the metric was from dollar) and it computes the structure. As long as the metric observes some basic principles (I think it's called triangle inequality) the spatial division will work and you can do a fast search for nearest neighbors.
http://depts.washington.edu/madlab/proj/dollar/index.html
which gives a value for how close to polylines are (it normalizes them and does some distance calculations)
the way the vp tree works is you provide a set of data and a metric for distance (in my case, the data was the polylines and the metric was from dollar) and it computes the structure. As long as the metric observes some basic principles (I think it's called triangle inequality) the spatial division will work and you can do a fast search for nearest neighbors.