| Professionals in the agricultural field, such as growers, Extension agents, and researchers, need a facility to organize and locate photographic images related to their work, especially as the volume of such images continues to increase. However, current keyword-based image retrieval suffers from relatively low precision and recall. A new approach to image retrieval using an ontology in the agricultural field addresses the limitation of supporting users to find proper images in keyword-based image retrieval, by browsing images associated with formal descriptions of the meanings of words and the relationships between them. Two hundred and ninety-one images were used to develop the approach in the particular domain including crops and related pests. A "crop-pest ontology" was created to represent concepts describing the images. The ontology contains crops and related pests, relationships between them, and environmental factors affecting them. A practical comparison between the crop-pest ontology and the existing National Agricultural Library Thesaurus (HALT) was done to compare and contrast the similarities and differences between the thesaurus and an ontology. The comparison shows that the crop-pest ontology has better formal representation capabilities avoiding ambiguity as well as supporting inferences which are not possible in a thesaurus such as NALT.; To enable browsing of images associated with the crop-pest ontology, images were indexed based on the ontology. The indexing process included manual syntactic and semantic analyses of each image caption, but such an analysis has a high labor cost. Therefore, a process of semi-automatic analysis was designed using natural language-based information extraction techniques which include a parser, a grammar described by phrase patterns, and the crop-pest ontology. A graphical interface was implemented for browsing images associated with concepts in the crop-pest ontology. This graphical interface (1) supports browsing images associated with concepts in the crop-pest ontology, (2) stores each image as an individual within the crop-pest ontology (3) supports visualization of all relationships in the crop-pest domain and (4) transfers domain knowledge to users browsing concepts. (Abstract shortened by UMI.)... |
