CS4487/9587 Homework Assignment #1

K-means and mean-shift


SLIC results
SLIC superpixels [Achanta et al., PAMI 2011]
This homework covers standard clustering methods (K-means and mean-shift) in the context of image segmentation. These methods are often used for color quantization or for computing superpixels. Generalization of K-means and mean-shift are also commonly integrated into more advanced segmentation methods.

You can start from an EZi-based K-means project implementing an interface that allows to load images, enter seeds, play with parameters, switch between RGB and RGBXY features, and save your results as images. To make this into a working algorithm, just implement functions Kmeans and init_means in file Kmeans.cpp. You can also use MATLAB or other software, as long as you write your own code for iterations of K-means algorithm. For MATLAB I can give some example of code using seed-based interface, let me know.

Specific Goals

What to submit

Submit a pdf file with your report (no more than 1.5 pages of text, but any number of images representing your results) summarizing your efforts in achieving the goals described above. In general, images of your resulst are highly encourages as they are an ideal way to represent your experiemnts in image analysis. The report should be submitted electronically via OWL. You should also submit your code (in a .zip file). The project report and code should be your independent effort.