Meeting time: Wednesdays 16:30 Location: G102A
Location: Karlovo namesti G3 (ground floor) at 16:30
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.
(Do not get confused by the text on the page. The mex version is in the package.)
Oxford 105k dataset: image thumbnails, descriptors (128 D vectors, single precision, stored one by one), and corresponing image names (one name per line, i-th name corresponds to i-th descriptors, Oxford 5k image names are given without a directory, remaining filenames contain a directory corresponding to the distributed directory structure).
Additional descriptors for Oxford105k are available here. These descriptors are based on deep convolutional neural networks and should in general give better visual similarity for nearest neighbours than the previous ones, please compare.