Name
Abstract | ||
|---|---|---|
The problem of compressing a large collection of feature vectors is investigated, so that object identification can be processed on the compressed form of the features. The idea is to perform matching of a query image against an image database, using directly the compressed form of the descriptor vectors, without decompression. Specifically, we concentrate on the Scale Invariant Feature Transform (SIFT), a known object detection method, as well as on Dense SIFT and PHOW features, that contain, for each image, about 300 times as many vectors as the original SIFT. Given two feature vectors, we suggest achieving our goal by compressing them using a lossless encoding by means of a Fibonacci code, for which the pairwise matching can be done directly on the compressed files. In our experiments, this approach improves the processing time and incurs only a small loss in compression efficiency relative to standard compressors requiring a decoding phase. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.tcs.2015.12.021 | Theor. Comput. Sci. |
Keywords | Field | DocType |
feature vectors,data compression,sift | Object detection,Scale-invariant feature transform,Computer vision,Feature vector,Pattern recognition,Artificial intelligence,Decoding methods,Data compression,Mathematics,Encoding (memory),Fibonacci number,Lossless compression | Journal |
Volume | Issue | ISSN |
638 | C | 0304-3975 |
Citations | PageRank | References |
2 | 0.43 | 28 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shmuel T. Klein | 1 | 434 | 77.80 |
| Dana Shapira | 2 | 144 | 32.15 |