Blog
Today I finished the code for subsampling from descriptors. This is needed, because it would take way too long to cluster on all descriptors.
Together with Ivo, I copied all the code to the Kameleon server here in Science Park. This server has 72GB internal memory, dual Quad core CPU's and 3 GPU cores. Obviously this will process the data a little bit faster than my Macbook Pro.
We fixed some small bugs in my code, copied all the Hollywood2 data to the server as well and the script to cluster all data is currently running. We use the same settings as the settings in the paper by Ivan Laptev (Learning realistic actions human actions from movies).
- 100k features, sampled from the training videos
- 4000 clusters
Hopefully we will end up with similar results as Ivan Laptev, so can we can easily make a comparison. Next up, I will start working on the code to create visual words using the calculated clusters.