Deciding the number of clusters in K-means clustering of descriptors I am new to the Machine Learning area and I have a question to ask. But let me first post the problem.

Problem:
The problem is very simple. I want to classify images as Category-1("Images containing garbage") or Category-2("Images not containing garbage"). Garbage is used in every literal sense of the word.

Solution I opted for:
The solution also happens to be pretty straightforward for the most part. Extract points of interest using an Algorithm like SIFT,SURF etc etc. Then obtain the descriptors of these key points and cluster them using the K-means algorithm. Then using this clustered data generate the bag of words and proceed from there.

The thing which I am unable to comprehend is the number of clusters which I might need. Any help with the above example would be much appreciated.
Measure, don't guess

It appears to be common to try k=1024, 2048, 4096, ... and use what works best for classification.

This is possivle because the clustering is actually a quantization task, and you do classification afterwards.