Knowledge-based image retrieval with spatial and temporal constructs

doi:10.1109/69.738355

Knowledge-based image retrieval with spatial and temporal constructs

1998-01-01

112

PRE

AI

DOI：10.1109/69.738355

原文链接

原文求助

摘要

En 中文

A knowledge-based approach to retrieve medical images by feature and content with spatial and temporal constructs is developed. Selected objects of interest in a medical image (e.g., x-ray, MR image) are segmented, and contours are generated from these objects. Features (e.g., shape, size, texture) and content (e.g., spatial relationships among objects) are extracted and stored in a feature and content database. Knowledge about image features can be expressed as a hierarchical structure called a Type Abstraction Hierarchy (TAH). The high-level nodes in the TAH represent more general concepts than low-level nodes. Thus, traversing along TAH nodes allows approximate matching by feature and content if an exact match is not available. TAHs can be generated automatically by clustering algorithms based on feature values in the databases and hence are scalable to large collections of image features. Further, since TAHs are generated based on user classes and applications, they are context- and user-sensitive. A knowledge-based semantic image model is proposed that consists of four layers (raw data layer, feature and content layer, schema layer, and knowledge layer) to represent the various aspects of an image objects' characteristics. The model provides a mechanism for accessing and processing spatial, evolutionary and temporal queries. A knowledge-based spatial temporal query language (KSTL) has developed that extends ODMG's OQL and supports approximate matching of feature and content, conceptual terms, and temporal logic predicates. Further, a visual query language has been developed that accepts point click-and-drag visual iconic input on the screen that is then translated into KSTL. User models are introduced to provide default parameter values for specifying query conditions. We have implemented a Knowledge-Based Medical Database System (KMeD) at UCLA, and it is currently under evaluation by the medical staff. The results from this research should be applicable to other multimedia information systems as well.

Keyword:

image database systems

visual query language

multimedia data modeling

knowledge-based query processing

temporal and spatial data modeling medical images

cooperative query answering content based image retrieval

Knowledge-based image retrieval with spatial and temporal constructs

摘要

AI总结

期刊

机构

引用论文