Name
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. Specifically, we concentrate on SIFT (Scale Invariant Feature Transform), a known object detection method. Given two SIFT feature vectors, we suggest achieving our goal to compress them using a lossless encoding for which the pair wise matching can be done directly on the compressed files, by means of a Fibonacci code. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/DCC.2014.53 | Data Compression Conference |
Keywords | Field | DocType |
image coding,image matching,object detection,transforms,Fibonacci code,compressed SIFT feature vectors,lossless encoding,object detection method,pair wise matching,query image matching,scale invariant feature transform,Compressed matching,Fibinacci codes,SIFT feature transform | Object detection,Computer vision,Scale-invariant feature transform,Feature vector,Pattern recognition,Feature detection (computer vision),Feature (computer vision),Computer science,Feature extraction,Artificial intelligence,Encoding (memory),Lossless compression | Conference |
ISSN | Citations | PageRank |
1068-0314 | 0 | 0.34 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shmuel T. Klein | 1 | 434 | 77.80 |
| Dana Shapira | 2 | 144 | 32.15 |