Implementations of image matching using "Scalable Recognition with a Vocabulary Tree" [closed]

Question

Do you know of any implementations or improvements of the method of matching images proposed by David Nister and Henrik Stewenius, called "Scalable Recognition with a Vocabulary Tree"? I am trying to implement it and I am having trouble understanding some parts of the algorithm (more specifically, computing the score).

Answer 1

Here is a good implementation of vocabulary tree - libvot. It use the C++11 standard multi-thread library to accelerate the build process so it runs pretty fast.

It uses three steps to build a vocabulary tree. The first step is to build a kmeans tree using sift descriptors. The second step is to build a image database using the vocabulary tree you build in the first step. The third step is to query the image against the image database. Some advanced techniques such as inverted list and L1 distance measure are also reflected in this repository.

Answer 2

Regarding the vocabulary trees, I found this thesis (http://www.tango-controls.org/Members/srubio/MasterThesis-VocabularyTree-SergiRubio-2009.pdf) which implements them in C++/python. However, I can't find the code anywhere, so I contacted the author to get the code but without success til this date.

Furthermore, I found this other implementation (http://www.inf.ethz.ch/personal/fraundof/page2.html), however I was unable to put it to work.

Have you implemented it already?? I would like to do the same for image recognition but it seems like a very painful task.

Best regards.