Peekaboo

Recently Jake showed some pretty cool videos in his blog . This inspired me to go back to an idea I had some time ago, about visualizing high-dimensional data via random projections. I love to do exploratory data analysis with scikit-learn , using the manifold , decomposition and clustering module. But in the end, I can only look at two (or three) dimensions. And I really like to see what I am doing.

After looking trough ImageNet for a little while now, I found some things that I did not really expect. So here are some properties of ImageNet that I found interesting (even though some of them might be obvious). But first, a quick recap on what ImageNet is: It's a hand annotated dataset, consisting of 10 million images with 10 thousand object classes. The images were collected using search engines and flickr. Classes correspond to "synsets" in WordNet. A synset is a collection of semantically equivalent nouns. For example, there is a synset called 'n04037443' (this is the IMID, the image net id), which corresponds to the nouns 'racer, race car, racing car' and is described as 'a fast car that competes in races'. The synsets in WordNet have an additional hierarchical structure, given by a directed graph. Going down the graph goes from more general concepts to more specific concepts. For example 'mammal' is above 'canine' whi...

Somehow I neglected writing about the CVML summer school (which is now what, 3 weeks ago?). I didn't really have the time to go through all of my notes yet. When I was at my notes on Alexei Efros' talk on Large Scale Recognition, I saw that he mentioned some joint work with Antonio Torralba on dataset biases in the computer vision community. The paper is called "Unbiased Look at Dataset Bias" ( pdf ) and appeared at CVPR this year. If you haven't heard of it: it is a MUST READ! Read it now! From the paper: "Disclaimer: No graduate students were harmed in the production of this paper. Authors are listed in order of increasing procrastination ability."