Automatic Extraction of Semantic Relationships from Images Using Ontologies and SVM Classifiers
- Title
- Automatic Extraction of Semantic Relationships from Images Using Ontologies and SVM Classifiers
- Author
- 이동호
- Keywords
- Content-based image retrieval; automatic image annotation; machine learning; ontology; support vector machine; semantic annotation
- Issue Date
- 2007-06
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- International Workshop on Multimedia Content Analysis and Mining, MCAM 2007: Multimedia Content Analysis and Mining, Page. 184-194
- Abstract
- Extracting high-level semantic concepts from low-level visual features
of images is a very challenging research. Although traditional machine
learning approaches just extract fragmentary information of images, their performance
is still not satisfying. In this paper, we propose a novel system that
automatically extracts high-level concepts such as spatial relationships or natural-
enemy relationships from images using combination of ontologies and SVM
classifiers. Our system consists of two phases. In the first phase, visual features
are mapped to intermediate-level concepts (e.g, yellow, 45 angular stripes).
And then, a set of these concepts are classified into relevant object concepts
(e.g, tiger) by using SVM-classifiers. In this phase, revision module which improves
the accuracy of classification is used. In the second phase, based on extracted
visual information and domain ontology, we deduce semantic relationships
such as spatial/natural-enemy relationships between multiple objects in an
image. Finally, we evaluate the proposed system using color images including
about 20 object concepts.
- URI
- https://link.springer.com/chapter/10.1007/978-3-540-73417-8_25https://repository.hanyang.ac.kr/handle/20.500.11754/106594
- ISBN
- 978-3-540-73416-1
- DOI
- 10.1007/978-3-540-73417-8_25
- Appears in Collections:
- COLLEGE OF COMPUTING[E](소프트웨어융합대학) > COMPUTER SCIENCE(소프트웨어학부) > Articles
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