6
$\begingroup$

I'm trying to use scikit-learn and pyssim for clustering a set of images - less than 100.

The end goal is to place the images into several buckets (clusters) according to the calculated similarity measures - CW-SSIM.

The task seems to be trivial, but I can't figure out the best way to handle "similarity based" clustering in scikit-learn. K-Means clustering looks like a good choice, but it doesn't accept any "comparison functions" or custom distance functions.

So how to handle the comparison based (similarity based) clustering in scikit-learn?

I was thinking about "comparison matrices" with 1 (similar) or 0 (not similar) per cell according to the calculated CW-SSIM similarity values. This matrix will be used for fitting into K-Means clustering. But then we will face the scalability issue, because such matrix will have dimensions equal to the amount of images ... which might grow to 1+ million in the future.

If there is an easier option in R than in Python, then I'm ready to review as well.

Thanks in advance.

UPDATE from Jan 18, 2016

I've created some code on GitHub about this topic: https://github.com/llvll/imgcluster

This project also includes IP[y] Notebook with step-by-step instructions and extra comments: https://github.com/llvll/imgcluster/blob/master/ip%5By%5D/imgcluster.ipynb

$\endgroup$
4
$\begingroup$

I would use a regular clustering algorithm and replace the objective function, which is usually the MSE, with a differentiable loss function of your choice. Another way is to learn an embedding that optimizes your similarity metric using a neural network and just cluster that.

If you would rather do similarity-based clustering, here are some papers:

  • A Similarity-Based Robust Clustering Method
  • A Discriminative Framework for Clustering via Similarity Functions
  • Similarity-Based Clustering by Left-Stochastic Matrix Factorization

sklearn implements two similarity clustering methods: Affinity propagation, and spectral clustering.

| improve this answer | |
$\endgroup$
4
$\begingroup$

It seems like you do not have fixed numbers of centroid(clusters) so centroid based clustering for example k-means can not be used in your case. However, you can use density based clustering for example DBSCAN.

| improve this answer | |
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.