Image matching in a large image collection

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There is about 26.000 images. I use only cropped art for detection. The best results were achieved with FAST/FREAK combination or SURF/SURF. I converted the descriptors to float and used flann with kmeans params when working with binary descriptors.

I have sample images, but no visualization of false positives.

I checked BoW, but I work mainly with binary descriptors due to the fact SURF/SIFT being non free. I had no success implementing this method yet due to errors i'm getting.

You need to group your local descriptors to a global one aka use some form of BoW to get good results. This is also possible with binary features, see http://answers.opencv.org/question/24835/is-there-a-way-of-using-orb-with-bow/#24858 .

An alternative way would be to use global descriptors in the first way, like the GIST descriptor, see http://people.csail.mit.edu/torralba/code/spatialenvelope/ .

Thank you for your help.

i think u can use template matching it is implemented on opencv http://docs.opencv.org/doc/tutorials/imgproc/histograms/template_matching/template_matching.html