How to use LibSVM for multiple descriptors for image classification - Matlab

Question

I need to classify pairs of image and indicate whether they're the same of not. I use several descriptors as SIFT LBP and more. I want now to use LIBSVM to do the training and test. how can I use teh svmTrain. should I save only the distance between 2 descriptors and then just have 1 1:SIftDelta, 2:LBPDelta

is this the correct way or is there any better approach? thanks

Answer 1

I'm not sure this is the right forum for this question, as it deals more with "high level" notions of learning, rather the specific implementation of it in Matlab.

Having said that, it seems like you are trying to combine multiple cues for learning, which is not a trivial task.

I can propose two methods for you:

1. Direct method - just concatenate all your descriptors into a single, very long, one and do the learning in this high dimensional space.

2. Do the learning in two stages (consequently, you'll have to partition your training data into two):

At the first stage, learn K classifiers, each using a different descriptor (assuming you wish to use K different descriptors).

Then, at the second stage, (using the reminder of your training data), you classify each example using the K classifiers you have: this will give you a new K -dimensional feature vector for each sample (you can put the classification result, or use the distance from the separating hyper plane to populate the k -th entry in the new descriptor). Now you can train a second classifier on the new K -dimension vectors. This second classifier gives you the final output of your multi-descriptor system.

-Enjoy!