Showing posts from May, 2012

Graphcuts for Python: pygco (slight update)

I have been using the excellent gco library for energy minimization with graph cuts for quite some time. Finally I got around to clean up / rewrite some of my Python wrappers so that maybe someone else can use them, too.

ICML 2012 Deep Learning and Unsupervised Feature Extraction Reading List

The ICML2012 accepted papers are officially online . On twitter , Andrej Kaparthy complained that the list is a bit hard to browse through. I agree and even though this is probably not the nice visualization he had in mind, I felt like having topical reading lists would somehow mitigate this problem. Here is my reading list on deep learning and unsupervised feature extraction: A Generative Process for Contractive Auto-Encoders Salah Rifai, Yann Dauphin, Pascal Vincent, Yoshua Bengio – Accepted Abstract: The contractive auto-encoder learns a representation of the input data that captures the local manifold structure around each data point, through the leading singular vectors of the Jacobian of the transformation from input to representation. The corresponding singular values specify how much local variation is plausible in directions associated with the corresponding singular vectors, whi

Superpixels for Python - pretty SLIC

Yesterday I wanted to try out a "new" superpixel algorithm that seemed quite successful: SLIC superpixels . This is actually a very simple algorithm, basically doing KMeans in the color+(x,y) space. I'm a bit bummed that they named that, since I already tried the same approach a couple of years ago and didn't think it was very useful. Well, apparently it is. The authors have a nice website with some examples. Unfortunately the linux binary didn't run on my box and building on linux seemed somewhat non-trivial. So I did what I always do: wrote some Python wrappers. You can find them on github [update] I did an implementation for scikit-image which is now quite mature thanks to some other contributors. I would recommend using that instead if you want SLIC in python.[/update]. The whole thing is pretty small, easy to build and easy to use. Also damn fast (less than a second per image). There are two variations, one where you can specify the number of superpi