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.
| Original language | English |
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| Title of host publication | Proceedings of the Prague Stringology Conference 2014, PSC 2014 |
| Editors | Jan Holub, Jan Zd'arek |
| Publisher | Prague Stringology Club |
| Pages | 139-147 |
| Number of pages | 9 |
| ISBN (Electronic) | 9788001055472 |
| State | Published - 2014 |
| Event | 18th Prague Stringology Conference, PSC 2014 - Prague, Czech Republic Duration: 1 Sep 2014 → 3 Sep 2014 |
Publication series
| Name | Proceedings of the Prague Stringology Conference 2014, PSC 2014 |
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Conference
| Conference | 18th Prague Stringology Conference, PSC 2014 |
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| Country/Territory | Czech Republic |
| City | Prague |
| Period | 1/09/14 → 3/09/14 |
Bibliographical note
Publisher Copyright:© Czech Technical University in Prague, Czech Republic.