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?


In my opinion, if you want a hybrid of Convolutional Neural Networks and the classic feature extraction techniques that would be redundant. Mainly because the architecture of a Convolutional Neural Network is composed of convolutions. I won't go much into detail of the whole architecture but these convolutions actually extract the good features for you and then those convolutions are connected to a classic Neural Network that does the classification task. Hence, extracting features using SIFT and using Convolutional Neural Networks would be redundant. In addition, the features that CNN will extract are better as compared to simple SIFT features. Though if you want to push through with combining SIFT and Deep Learning, you can instead substitute convolutions and use SIFT for feature extraction and then feed it into a Neural Network. That would work as well.


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