Name
Title | ||
|---|---|---|
Very large scale multidimensional data management and retrieval for USGS and NIMA imagery |
Abstract | ||
|---|---|---|
Content-based image retrieval using low-level features such as color, texture and shape has been well studied. Various image querying systems have been built based on the low-level features for general or specific image retrieval tasks. The application of these approaches in geographic images have been explored, e.g. [1]. However, retrieving images based on low-level features may not be satisfactory. With the enormous growth of GIS images, it is an urgent need to build image retrieval systems which support both low-level (feature-based) and high-level (semantics-based) querying and browsing of images. |
Year | Venue | Keywords |
|---|---|---|
2004 | DG.O | urgent need,geographic image,retrieving image,large scale multidimensional data,gis image,nima imagery,enormous growth,content-based image retrieval,low-level feature,specific image retrieval task,image retrieval system,various image |
Field | DocType | Citations |
Computer vision,Automatic image annotation,Information retrieval,Computer science,Image retrieval,Artificial intelligence,Data management,Visual Word | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aidong Zhang | 1 | 2970 | 405.63 |
| Wei Wang | 2 | 51 | 4.27 |
| David M. Mark | 3 | 963 | 130.62 |