How to use SIFT/SURF as features for a machine learning algorithm?

opencv image-processing machine-learning sift feature-extraction
sreeraag picture sreeraag · Nov 18, 2013 · Viewed 7.6k times · Source

Im working on an automatic image annotation problem in which im trying to associate tags with images. For that im trying for SIFT features for learning. But the problem is all the SIFT features are a set of keypoints, each of which have a 2-D array, and the number of keypoints are also huge.How many and how do I give them for my learning algorithm which typically accepts only one-d features?

Answer

old-ufo picture old-ufo · Nov 18, 2013

You can represent single SIFT as "visual word" which is one number and use it as SVM input, I think it is what you need. It is usually done by k-means clustering.

This method is called "bag-of-words" and described in this paper.

Short presentation review of method.

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