A knowledge-based approach is introduced for retrieving images by content. It supports the answering of conceptual image queries involving similar-to predicates, spatial semantic operators, and references to conceptua...
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A knowledge-based approach is introduced for retrieving images by content. It supports the answering of conceptual image queries involving similar-to predicates, spatial semantic operators, and references to conceptual terms. interested objects in the images are represented by contours segmented from images. Image content such as shapes and spatial relationships are derived from object contours according to domain-specific image knowledge. A three-layered model is proposed for integrating image representations, extracted image features, and image semantics. With such a model, images can be retrieved based on the features and content specified in the queries. The knowledge-based query processing is based on a query relaxation technique. The image features are classified by an automatic clustering algorithm and represented by Type Abstraction Hierarchies (TAHs) for knowledge-based query processing. Since the features selected for TAH generation are based on context and user profile, and the TAHs can be generated automatically by a clustering algorithm from the feature database, our proposed image retrieval approach is scalable and context-sensitive. The performance of the proposed knowledge-based query processing is also discussed.
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, an...
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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 oth
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