@inproceedings{5ddf1e7909044337866afde07435c4a9,
title = "Metric preserving Dense SIFT compression",
abstract = "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. Given two SIFT feature vectors, in previous work we suggested to compress them using a lossless encoding for which the pairwise matching can be done directly on the compressed files, by means of a Fibonacci code. In this paper we extend our work to Dense SIFT and in particular to PHOW features, that contain, for each image, about 300 times as many vectors as the original SIFT.",
author = "Klein, \{Shmuel T.\} and Dana Shapira",
note = "Publisher Copyright: {\textcopyright} Czech Technical University in Prague, Czech Republic.; 18th Prague Stringology Conference, PSC 2014 ; Conference date: 01-09-2014 Through 03-09-2014",
year = "2014",
language = "الإنجليزيّة",
series = "Proceedings of the Prague Stringology Conference 2014, PSC 2014",
pages = "139--147",
editor = "Jan Holub and Jan Zd'arek",
booktitle = "Proceedings of the Prague Stringology Conference 2014, PSC 2014",
}