Low recognition rates using GMM for image classification I want to use GMM for image classification. 
So, I have extracted SIFT features from each image in the corpus.
Then, I apply EM algorithm to learn GMM parameters (I have coded it in matlab).
I get very low Recognition rates for unseen images when I test the model on larger number of classes (>=4).
Who can explain this result ?  Thanks.
Are you trying to model the distribution of the SIFT descriptors using GMM? Are you using the actual 128-element descriptors?  If so, then you are trying to model a distribution in a 128-dimensional space, which is not likely to work well.

You need to reduce the dimensionality. If you insist on using GMM, then the simplest thing to try is to use principal components analysis (PCA) to reduce the dimensionality of your descriptors.  I would not use more than 10 dimensions.  Here is a paper, that tried kernel density estimation on reduced SIFT descriptors among other things.  

Alternatively, you could try other algorithms, such as Bag Of Features, or the Pyramid Match Kernel.