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Hi guys, during this week i've made some other tests. The last step i've done was BOW implementation (but i stopped as soon as i read the answer of Guanta about binary descriptors & BOW). I've made some searches on BBOW (Binary Bag of word); i found some good pdfs, but not a code implementation of BBOW.

So i've change my code from BRISK+FREAK+FLANN to ORB+ORB+BFMACTHER(NORM_HAMMING) as described in this good post of stack overflow trying to have (with high hopes) better results http://stackoverflow.com/questions/9539473/opencv-orb-not-finding-matches-once-rotation-scale-invariances-are-introduced

But i have no success (sigh, sob). So i think that the best way to proceed is taking the suggestion of Guanta: download master branch of OpenCV (rel 3.0) and use KAZE+BOW I've load an image that collects all images i'm working on for find image similarities. I've grouped images that for me are similar with a border with different color (for me similar means "same subject in different photos"). http://imagizer.imageshack.us/v2/xq90/855/s8lq.jpg

The best results i got (BEFORE ORB implementation) was that the dog of set1 are correctly matched with himself, but it's matched with one photo of set2 also. And if i use ONLY the three photos of set2 and try to match between them, i never find a good match for anyone (but the subject is the same)

I have better results with images in set 3 and 4, but for the set 3 images, not all images are matched correctly (this means that one of them is not selected as similar). So the question: is KAZE+BOW the better (& only) way to proceed in order to find similar images? Or probably what i'm trying to achieve is not possible?

Thanks a lot again Andrea