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Full-Automatic High-Level Concept Extraction from Images Using Ontologies and Semantic Inference Rules

Title
Full-Automatic High-Level Concept Extraction from Images Using Ontologies and Semantic Inference Rules
Author
이동호
Keywords
Visual Feature; Image Retrieval; Inference Rule; Resource Description Framework; CBIR System
Issue Date
2006-09
Publisher
SPRINGER-VERLAG BERLIN
Citation
Asian Semantic Web Conference; ASWC 2006: The Semantic Web – ASWC 2006, Page. 307-321
Abstract
One of the big issues facing current content-based image retrieval is how to automatically extract the semantic information from images. In this paper, we propose an efficient method that automatically extracts the semantic information from images by using ontologies and the semantic inference rules. In our method, MPEG-7 visual descriptors are used to extract the visual features of image which are mapped to the semi-concept values. We also introduce the visual and animal ontology which are built to bridge the semantic gap. The visual ontology facilitates the mapping between visual features and semi-concept values, and allows the definition of relationships between the classes describing the visual features. The animal ontology representing the animal taxonomy can be exploited to identify the object in an image. We also propose the semantic inference rules that can be used to automatically extract high-level concepts from images by applying them to the visual and animal ontology. Finally, we discuss the limitations of the proposed method and the future work.
URI
https://link.springer.com/chapter/10.1007/11836025_31http://repository.hanyang.ac.kr/handle/20.500.11754/108543
ISBN
978-3-540-38329-1
DOI
10.1007/11836025_31
Appears in Collections:
COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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