Hierarchy-Based Networked Organization, Modeling, and Prototyping of Semantic, Statistic, and Numeric Image Information

Hussain Sabri SHAKIR  Makoto NAGAO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E78-D   No.8   pp.1003-1020
Publication Date: 1995/08/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Databases
Keyword: 
image databases,  pictorial semantics,  image prototyping,  entity modeling,  

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Summary: 
This paper presents a comprehensive framework for the organization, retrieval, and adaptation of image information and meta-information in image database systems. The multi-level hierarchy of images that emphasizes the composition of visual entities (such as Human, Chair, , etc.) from its constituents (eye, leg, , etc.) is managed by a host architecture that is called the semantic tree. This architecture is shown to integrate description, numeric, and statistic image constituent's information into a compound space that is used as retrieval basis for semantic, sketch, and template image queries and several other composite query types. The core architecture based on which the semantic tree is constructed is shown to offer several new features such as simple prototyping, complex prototyping, low storage requirements, and automatic knowledge acquisition compatibility. The object oriented data model constitutes our comparison basis throughout the paper. Methods (functions) used to access image information are shown to be organized into a separate architecture called the query dictionary. This architecture is shown to offer a convenient hierarchical message passing medium using which a variety of composite queries are constructed. Interaction between semantic trees and the query dictionary is clarified through several examples. It is shown that the semantic tree architecture embraces additional networking semantic intormation through a range of relation representation models, the first of which is introduced in this paper. A new inheritance method called semantic relation spreading is introduced. Comprehensive examples are given to demonstrate the versatility of the new strategy.