Semaine 8 lundi

~ lundi ~

Still labeling data for attribute vectors... Just labeled for 2 hours and realized we've been doing it the wrong way. Haha. So wrote a script to do it... Originally tried to avoid having to write the script, but still coming back to it, it's actually not so bad. It might actually be faster.

The new labeling would expand the dataset from 57 images to 228 images, with 76 images per category, Italian (pasta + pizza), Japanese (sushi + sashimi), and Korean (bibimbap + bulgogi). Images are hand-cropped to food area so that we get as little background as possible.

Use 3 low-level features: EMD, RGB color histogram, SIFT.
EMD because it worked well above chance before, so see how well it'll work in attribute-based classification.
RGB color histogram just because.
SIFT, just to try it, because color information is very important for food, and SIFT would discard color information with grayscale images being the input.
It doesn't really make sense to try any other features that discard color information, before seeing how well SIFT would work...

RGB hist and SIFT data are already calculated and outputted to file for loading back into training, just waiting for labeling to be done, so that they can be trained. EMD needs the labeling for the clusters before it can spit out data.

With the auto-script, it should be quicker labeling the attribute vectors. Still need to label the clusters with the corresponding ingredients for the new images though, to feed to the script.