CBIR indexing strategy

Question

I'm developing a CBIR solution to be integrated in a license plate recognition application. The image matching algorithm is very robust, but as you can imagine the database is huge and the extraction of images for matching from a database is really slow. I've tried to quantize an image in something like a small local feature vector or even a single numerical value, but without sucess. The idea is to index some such value, to allow really fast extraction, while simultaneously reducing greatly the number of matching candidates. I've read a lot of papers on the subject, but most of them address classification and machine learning as a solution. Since I am not seeing how classification can be useful, since all the images are pretty similar to each other (license plate pictures), I would like to discuss ideas with someone who's had a similar problem in the past, or even someone who has some clue on how I can solve this. I've been really trying to engineer my way out of this performance issue for a long time, but without much sucess.

Answer 1

Given the additional information in the comments, I would solve the problem in the following way:

• Detect/segment the plate from the image;

• Apply OCR in order to extract a string with the letters and number from the plate;

• In order the verify if two images corresponds to the same license plate, compare the two strings. Note also that it is much easier and efficient to index strings when compared to multi-dimensional feature vectors.