clustering with uncertain number of clusters I detect a unmanned aerial vehicle(UAV) in a picture using template matching. The template library only contains targets with different scales, rotations and other differences.I want to simplify the template library using clustering. I learned the k-means and Gaussian mixture model（GMM）but they all need determine the value of k.Are there any approaches which can solve my problem?
Several algorithms allow doing this.
First one is the hierarchical clustering. When creating your dendrogram, the key is to cut the "longest branches." DBSCAN is also a good alternative. Finally, you can use K-means or GMM and optimize your number of cluster against a metrics (see sklearn pages on the subject) (be aware of overfitting)