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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?

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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)

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