How to use SIFT descriptors with Locality Sensitive Hashing?

Question

LSH is used with binary strings and is very efficient in indexing and retrieval. While SIFT is not a binary descriptor, it is still the best descriptor available there. Is it possible to use SIFT with LSH ? Also, are there better indexing techniques available for SIFT features ?

I have tried using SIFT with FLANNBased Matcher with LSH in OpenCV, but get the following error:

what(): C:\OpenCV\opencv\modules\flann\src\miniflann.cpp:315: error: (-210) type=5 in function buildIndex_

Code:

cv::Ptr<cv::FlannBasedMatcher> matcher = new cv::FlannBasedMatcher(new cv::flann::LshIndexParams(5, 24, 2));
        matcher->match( descriptors, descriptors1, matches );

Note: This question was more apt at dsp.stackexchange.com, but it is in read only mode right now.

SIFT is indeed the best descriptor in view-point changes, but BRIEF outperforms it in photometric changes. Also, I think that if you'll add a rotation compensation mechanism to BRIEF, it will give nice results in view-point changes. — GilLevi

sansuiso sansuiso · Accepted Answer · 2013-11-09T20:46:24

LSH is an algorithm of dimensionality reduction that outputs binary strings. It was designed to index real-valued, high-dimensional data (but with an intrinsic lower dimensionality, such as manifolds) with binary codes.

You can try to implement LSH by yourself so that it can work with SIFT descriptors. The naive and easiest way of doing it would be to use random projections, but some more clever scheme using the fact that a SIFT descriptor is an aggregate of gradient orientation histograms can probably be exploited to produce a more efficient hash function.

How to use SIFT descriptors with Locality Sensitive Hashing?

2 Answers