Direct Processing of Compressed SIFT Feature Vectors

The problem of compressing a large collection of feature vectors so that object identification can further be processed on the compressed form of the features is investigated. The idea is to perform matching against a query image in the compressed form of the feature descriptor vectors retaining the metric. Specifically, we concentrate on SIFT (Scale Invariant Feature Transform), a known object detection method. Given two SIFT feature vectors, we suggest achieving our goal to compress them using a lossless encoding for which the pair wise matching can be done directly on the compressed files, by means of a Fibonacci code.