Name
Abstract | ||
|---|---|---|
Extraction of stable local invariant features is very important in many computer vision applications, such as image matching, object recognition and image retrieval. Most existing local invariant features mainly characterize luminance information, and neglect color information. In this paper, we present a new local invariant descriptor characterizing both of them, which combines three photometric invariant color descriptors with the famous SIFT descriptor. To reduce the dimension of the combined high-dimensional invariant feature the principal component analysis (PCA) is used. Our experiments show the proposed local descriptor through combining luminance and color information outperforms the descriptors that only utilize a single category of information, and combining the three color feature representations is more effective than only one. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICME.2007.4284948 | ICME |
Keywords | Field | DocType |
effective local invariant descriptor,color information,image matching,luminance information,feature extraction,image retrieval,object recognition,computer vision,sift descriptor,stable local invariant features,photometric invariant color descriptors,principal component analysis,image colour analysis,robustness,lighting,photometry,geometry | Scale-invariant feature transform,Computer vision,Pattern recognition,Computer science,Image retrieval,Feature extraction,Robustness (computer science),Artificial intelligence,Invariant (mathematics),Luminance,Principal component analysis,Cognitive neuroscience of visual object recognition | Conference |
ISBN | Citations | PageRank |
1-4244-1017-7 | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Zhang | 1 | 125 | 17.08 |
| Weiqiang Wang | 2 | 469 | 49.23 |
| Wen Gao | 3 | 11374 | 741.77 |
| Shuqiang Jiang | 4 | 1233 | 98.27 |