OLYBIA: Ontology-Based Automatic Image Annotation System Using Semantic Inference Rules
- Title
- OLYBIA: Ontology-Based Automatic Image Annotation System Using Semantic Inference Rules
- Author
- 이동호
- Keywords
- high-level concepts; ontologies; semantic inference rules; MPEG-7 visual descriptors; semantic gap
- Issue Date
- 2007-04
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- International Conference on Database Systems for Advanced Applications, DASFAA 2007: Advances in Databases: Concepts, Systems and Applications, Page. 485-496
- Abstract
- One of the big issues facing current content-based image retrieval is
how to automatically extract the high-level concepts from images. In this paper,
we present an efficient system that automatically extracts the high-level
concepts from images by using ontologies and semantic inference rules. In our
method, MPEG-7 visual descriptors are used to extract the visual features of
image, and the visual features are mapped to semi-concepts via the mapping
algorithm. We also build the visual and animal ontologies to bridge the
semantic gap. The visual ontology allows the definition of relationships among
the classes describing the visual features and has the values of semi-concepts as
the property values. The animal ontology can be exploited to identify the highlevel
concept in an image. Also, the semantic inference rules are applied to the
ontologies to extract the high-level concept. Finally, we evaluate the proposed
system using the image data set including various animal objects and discuss
the limitations of our system.
- URI
- https://link.springer.com/chapter/10.1007/978-3-540-71703-4_42https://repository.hanyang.ac.kr/handle/20.500.11754/106402
- ISBN
- 978-3-540-71702-7
- DOI
- 10.1007/978-3-540-71703-4_42
- Appears in Collections:
- COLLEGE OF COMPUTING[E](소프트웨어융합대학) > COMPUTER SCIENCE(소프트웨어학부) > Articles
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