Started working on K nearest neighbor classification.
Determined the best recognition rate with k = 6, which yields 36% recognition rate. Currently investigating methods for expanding the method from 1 to 1 relations to many to many relations between segment sets. Using the graph structure to restrict the number of subsets to simple paths in the adjacency graph, using associativity of barycenter and some dynamic programming for implementation, I should be able to implement it very efficiently.
Started working on palette projection, the idea being to solve the problem of different characters with similar color palettes by spectral embedding of the palettes with modulated similarities.
Lots of work on making palette projection classification work. Also adding new samples to the dataset.
Finally finished erasing background from new test images.
Now added masks for new samples in the dataset. Segment matching classification now reaches 53% leave one out recognition rate ! Although palette projection still sucks, probably because of the lack of unlabeled samples and/or a bug in the implementation somewhere. Will try increasing the number of samples per class even more and see how recognition rate evolves - although it is quite boring.
Added 5 additional images for each class, now reaches 59% leave one out recognition rate. It gives especially good results for luffy, rei, miku, asuka and amuro for some reason.
Cleanup of debugging display.