Inverted index compression for scalable image matching (2010)
| Venue: | in [Proc. Data Compression Conference (DCC’10 |
| Citations: | 4 - 1 self |
BibTeX
@INPROCEEDINGS{Chen10invertedindex,
author = {David M. Chen and Sam S. Tsai and Vijay Ch and Gabriel Takacs and Ramakrishna Vedantham and Radek Grzeszczuk and Bernd Girod},
title = {Inverted index compression for scalable image matching},
booktitle = {in [Proc. Data Compression Conference (DCC’10},
year = {2010},
pages = {525}
}
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Abstract
To perform fast image matching against large databases, a Vocabulary Tree (VT) uses an inverted index that maps from each tree node to database images which have visited that node. The inverted index can require gigabytes of memory, which significantly slows down the database server. In this paper, we design, develop, and compare techniques for inverted index compression for image-based retrieval. We show that these techniques significantly reduce memory usage, by as much as 5×, without loss in recognition accuracy. Our work includes fast decoding methods, an offline database reordering scheme that exploits the similarity between images for additional memory savings, and a generalized coding scheme for soft-binned feature descriptor histograms. We also show that reduced index memory permits memory-intensive image matching techniques that boost recognition accuracy. 1.
