In this assignment, you will segment images by color, using the K-means algorithm and some variants. Here is a picture of high quality results on this assignment.
What You Should Do
- First implement a basic K-Means Clustering Algorithm (your own
code) using the RGB color space the images come in.
- First with randomly selected cluster seeds
- Next try sampling pixels from the image to find the seeds. Choose a pixel and make its value a seed if it is sufficiently different from already-selected seeds. Repeat till you get K different seeds.
- Next with a method that you develop for selecting the seeds intelligently from the image using its color histogram (again your code). The seed selection should be automatic, given the histogram and the number of seeds to be selected. One way to go is to find the peaks in the color histogram as candidates for seeds.
- First with randomly selected cluster seeds
- Now develop and implement a smarter K-Means variant (your own
code again)
that determines the best value for K by evaluating statistics of the
clusters.
Some possible methods to try:
- You can start from the color histogram, as K is closely
related to the number of peaks in the histogram. Not all the peaks are
necessary as you want only the dominant ones, so you should pick the
ones that occupy a certain fraction of the image in terms of pixels.
- You can also try clustering using different Ks, and pick the
best one. The metric could be related to the distance between clusters
and the variance within each cluster.
- You are free to come up with your own ways.
- You can start from the color histogram, as K is closely
related to the number of peaks in the histogram. Not all the peaks are
necessary as you want only the dominant ones, so you should pick the
ones that occupy a certain fraction of the image in terms of pixels.
- Test each variant of the above on the following
scene images and report your results:
s03, s06, s08, s09, s12.
1. Replace each pixel's value with the number of the cluster to which it has been assigned.
2. Use the provided autocolor function to transform into something more easily interpretable.
What You Should Turn In
1. All of your code, which must be well commented so that the grader can compile it to working binaries.2. Write a brief report on the performance of:
- the basic K-means with a) random seeds, b) sampled seeds, c) seeds selected using histogram.
- your smart K-means algorithm
You report must clearly describe and explain the algorithms you developed and also include some discussions on failure examples or limitations for your approach; this will shed light on future improvements. It must include output results showing original images and color clusters for the different methods. It can be a Word document or pdf document.
Download .doc template for the report (HW3-report.doc). You are free to use other text processing tools like latex etc, however make sure that you have the same sections in your report.