In the application I am developing, I have about 5000 product label images.(One label per product).
One functionality of my application is that user can take a picture using his camera and get a possible match(es) against the product labels registered the system.
Since initially, my system only has one sample per product, I decided to go with traditional Computer Vision techniques. I managed to implement this using Feature extraction and Descriptor matching.(using OpenCV SIFT and FLANN techniques referring this: https://github.com/kipr/opencv/blob/master/samples/cpp/matching_to_many_images.cpp)
Now I am thinking how to improve the accuracy by combining with CNN or Deep Learning techniques since when users approve matches, it gradually add more label samples for a product.
Is it possible to build a hybrid image matching system combining Computer Vision techniques and CNN/Deep Learning techniques?
Are there any similar services already available as services?