This is my first image processing application, so please be kind with this filthy peasant.
THE APPLICATION:
I want to implement a fast application (performance are crucial even over accuracy) where given a photo (taken by mobile phone) containing a movie poster finds the most similar photo in a given dataset and return a similarity score. The dataset is composed by similar pictures (taken by mobile phone, containing a movie poster). The images can be of different size, resolutions and can be taken from different viewpoints (but there is no rotation, since the posters are supposed to always be right-oriented).
Any suggestion on how to implement such an application is well accepted.
FEATURE DESCRIPTIONS IN OPENCV:
I've never used OpenCV and I've read this tutorial about Feature Detection and Description by OpenCV.
From what I've understood, these algorithms are supposed to find keypoints (usually corners) and eventually define descriptors (which describe each keypoint and are used for matching two different images). I used "eventually" since some of them (eg FAST) provides only keypoints.
MOST SIMILAR IMAGE PROBLEM AND LSH:
The problems above doesn't solve the problem "given an image, how to find the most similar one in a dataset in a fast way". In order to do that, we can both use the keypoints and descriptors obtained by any of the previous algorithms. The problem stated above seems like a nearest neighbor problem and Locality Sensitive Hashing is a fast and popular solution for find an approximate solution for this problem in high-dimensionality spaces.
THE QUESTION:
What I don't understand is how to use the result of any of the previous algorithms (i.e. keypoints and descriptors) in LSH.
Is there any implementation that treat this problem?