TY - GEN
T1 - Metric preserving Dense SIFT compression
AU - Klein, Shmuel T.
AU - Shapira, Dana
N1 - Publisher Copyright:
© Czech Technical University in Prague, Czech Republic.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84978413850&partnerID=8YFLogxK
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AN - SCOPUS:84978413850
T3 - Proceedings of the Prague Stringology Conference 2014, PSC 2014
SP - 139
EP - 147
BT - Proceedings of the Prague Stringology Conference 2014, PSC 2014
A2 - Holub, Jan
A2 - Zd'arek, Jan
T2 - 18th Prague Stringology Conference, PSC 2014
Y2 - 1 September 2014 through 3 September 2014
ER -