Extracting spatial semantics in association rules for ocean image retrieval

Several research institutions and governmental departments provide ocean images for research purposes. For example, Argo, a worldwide ocean research organization, produces ocean salinity and temperature images and researchers can download those images from the Internet. One may build an image system to store ocean images and retrieve them later for further research, for example, to predict future salinity or temperature variation. Image retrieval technology is therefore important. This paper describes an ocean image retrieval system based on content-based image retrieval. Currently, content-based image retrieval technology does not exploit high-level semantics, and it is hard to obtain predictive information from retrieved images. Our improvement involves a spatial reference method that is used to help get the spatial relationships between objects for a certain image. This allows the spatial semantics between the query image and images in database to be considered. Spatial association rules are also mined and are subsequently used as a basis for retrieving additional images. As the spatial semantics in both the query image and spatial association rules, the retrieved images are more accurate. The experimental results verify that the system effectively predicts the occurrence of salinity or temperature variations.