garner1 / repo_bag_of_features

Use TDA to study breast cancer images.

BAG-OF-FEATURES FOR TRANSCRIPTOMIC IMAGE-CLASSIFICATION """ Created on Thu Apr 9 16:45:36 2015

@author: silvano """ The idea is to use a bag-of-features approach to classify cases.

Pipeline:

1. extract_SIFT_descriptor.py: Extract SIFT descriptors
2. filter_and_see_features.py: Filter descriptors wrt size and removes inter-channel overlapping keypoints
3. data_analysis_{kmeans,minibatchkmeans,NMF,PCA}.py: partition data according to some algo. At the moment only kmeans, minibatchkmeans, PCA work well.
4. classification.R: classify cases (find the right distance measure btw histograms, see scipy.distance)