iqm stands for inverted-quantized k-means (IQ-means) and implements a method for fast approximate clustering.
iqm has a 2-clause BSD license. See file LICENSE for the complete license text.
iqm constists primarily of Matlab .m files, but also includes a number of .cpp files which are compiled as mex files to interface Matlab. The directory structure is:
/anim animation of algorithm iterations on 2D example
/config project configuration, including datasets, paths etc.
/disp text/graphical display
/lib generic utilities library
/quant full set of learning, encoding, inversion and search methods
/sub underlying code handling subspaces
/test main test entry points
/util specific utilities for this application
iqm requires Matlab, an appropriate C++ compiler to compile mex files (e.g. gcc), yael library, and xio library.
After installing yael, choose a location on disk, say home, where to store datasets and output. Make one subfolder for each dataset with the name of the dataset, say sift, and copy the raw dataset files in a subfolder named raw. For instance, download the SIFT1M dataset and unzip its contents into
home/sift/raw/
iqm uses the specific filenames and file formats as given in the link above. A number of additional folders for output will be created automatically under home/sift/.
Finally, edit /config/local.m and adjust the location of home and yael to your local settings. Now you can try any of the samples under /test/. The mex files should compile automatically.
Please cite the following paper if you use this software.
Y. Avrithis, Y. Kalantidis, E. Anagnostopoulos, and I. Z. Emiris. Web-scale image clustering revisited. In Proceedings of International Conference on Computer Vision (ICCV 2015), Santiago, Chile, December 2015.