Meeting time: Wednesdays 16:15 Location: G102A
The following lines will read the descriptors and the image names in MATLAB:
fid = fopen('imagedesc.dat', 'r');
X = fread(fid, [128,inf], 'single⇒single');
Names = textread('imagenames.txt', '%s');
SIFT dataset: 2M SIFT descriptors are available here. The descriptors are 128D unsigned byte precision, the following Matlab lines will read the descriptors:
fid = fopen('SIFT.dat', 'r');
X = fread(fid, [128,inf], 'uint8⇒uint8');
Use the SIFT dataset for the approximate k-means. Use 32k cluster centers. Compare three different assignments to the nearest cluster (kd-forest, k-means tree, exact assignmet). For all three cases, start from identical inicialization. Compare the final results (up to say 30 iterations) in terms of sum of squared distances, that is Σ (X - f(X))^2, where f(X) is the assigned cluster center.
Looking forward to results on your own data too.