33 0

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_25http://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] > COMPUTER SCIENCE(소프트웨어학부) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE