kNN Various Stages Explained - Training Phase

Training Phase:

Training Image is /usr/sci/crcnsdata/CRCNS/Synapses/data/Refined2_marked_RM2_with_fake/Layer1_0_0_card_resize_p25.tif
Size of the image is 4590 x 2869 (downSampled 4x4 from original)

Step 1: Generating SIFT key points
We generate the SIFT key points for this image using /usr/sci/crcnsdata/CRCNS/Synapses/Code/Matlab/sift/sift.m
Time taken : 6 minutes
Number of SIFT key points = 359265

Step 2: Converging the SIFT key points
Filter of Key points in the brighter part of the image, and the borders and converge the rest. These operations are done using /usr/sci/crcnsdata/CRCNS/Synapses/Code/Matlab/kNN/CentroidCalc2.m
Time taken : 5 minutes
After this filtering the number of points = 69627
Unique number of SIFT points = 56521
After converging the points the number of unique points = 56467

Step 3: Clustering the points
The new cluster-center initialization method is used. The algorithm picks the first cluster center point randomly and then chooses the next points as the one farthest from the already identified cluster centers. The algorithm stops when the greatest separation of a point is lesser than specified separation from any of the cluster centers identified. The algorithm ensures that there no point that is further than the specified separation distance from a cluster center. The algorithm is in /usr/sci/crcnsdata/CRCNS/Synapses/Code/Matlab/kmeans/kmeans.m
Time taken : 87 seconds
Separation distance = 17.5 pixels
Number of clusters identified = 8395
The 8395 cluster points identified.