The State University of New York at Buffalo is developing a geographic image retrieval system termed GiView. From a large-scale geographic image database, the system retrieves relevant images that contain parts similar to a given query image. The system extracts texture and color features of geographic images. One key design of the system is a practical image decomposition scheme called nona-tree that uses a hierarchical overlapping window structure. This treatment offers a compromise to alleviate the difficulty of object recognition which is still an open problem in computer vision. The system also takes the advantage of multi-resolution properties of wavelet transforms so that the system can extract texture features in different scales. This design takes into account the multi-scale nature of geographic images and improves retrieval efficiency. A third key design of the system is to cluster database images based on their similarity to a set of geographic image templates. The templates are selected according to needs of particular projects and multiple cluster approaches are implemented to accommodate different needs.
|Original language||English (US)|
|Number of pages||1|
|Journal||Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM|
|State||Published - Jan 1 1999|
All Science Journal Classification (ASJC) codes
- Applied Mathematics