Object Recognition with SIFT - to inaccurate??

asked 2013-09-24 13:55:10 -0600

mike gravatar image

updated 2013-09-24 14:00:16 -0600

berak gravatar image


I'm new in OpenCV and I try to do an object recognition based on SIFT. My final goal is an automatic object detector, which gets an image from a camera and can match this with other images which are already stored in a database. Or if it doesn't find an appropriate match, it should create a new ObjectID for this Object in its database and learn it.

I tried to start with a simple matching of SIFT descriptors I extracted from some sample Images to find out which images belong together (FLAN matcher). The results are very disappointing. I took pictures of a face and a bottle of water from different views. When I tried to match the objects, I could barely detect a big difference between them. The feature points were often detected on homogeneous areas where the matching then went completely wrong as there couldn't be found the corresponding patch in the compared image.

My question is now, do you think it's possible to design a object detector like this - and if, how can I improve the matching performance? I planned to use a Bag of Features approach with an SVN classifier, but I am afraid that the performance will still be poor. Better Ideas?


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I think you can get good results on object classification using BOW+SVM. I got nice results using a matlab package, but the algorithm is the same.

GilLevi gravatar imageGilLevi ( 2013-09-24 15:26:57 -0600 )edit