greedy-point-matching

name	greedy-point-matching
description	Match clustered centroids to expert annotations using greedy nearest-neighbor matching with distance constraints. Compute F1 scores and delta metrics for clustering quality assessment.

Greedy Matching Algorithm

Match predicted cluster centroids to expert annotations for a single image:

1. Initialize:

   • Set of predicted centroids (from DBSCAN clustering)
   • Set of expert points (ground truth annotations)
   • Maximum allowed distance threshold = 100 pixels

2. Greedy Loop:

   • While there are unmatched predictions and experts:
     • Find the pair (prediction, expert) with minimum standard Euclidean distance
     • If distance > 100 pixels, stop matching
     • Mark this pair as matched and remove from consideration
     • Record the distance for this match

3. Output:

   • List of matched pairs and their distances
   • Count of true positives, false positives, false negatives

F1 Score Calculation

For a single image:

TP = number of matched pairs
FP = number of unmatched predictions
FN = number of unmatched expert points

Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
F1 = 2 * (Precision * Recall) / (Precision + Recall)

Handle edge cases:

• If TP = 0: F1 = 0.0
• If Precision + Recall = 0: F1 = 0.0

Delta Metric

Delta = average standard Euclidean distance of all matched pairs:

delta = mean(distances of matched pairs)

If no matches found for an image, delta = NaN.

Distance Metric Note

Important: Always use standard Euclidean distance for matching and delta calculation:

euclidean(a, b) = sqrt((Δx)² + (Δy)²)

This is different from the custom distance metric used in DBSCAN clustering.