Automatic extraction of semantic relationships from images using ontologies and SVM classifiers

Jin Woo Jeong; Kyung Wook Park; Ouk Seh Lee; Dong Ho Lee

doi:10.1007/978-3-540-73417-8_25

Automatic extraction of semantic relationships from images using ontologies and SVM classifiers

Jin Woo Jeong
, Kyung Wook Park
, Ouk Seh Lee
, Dong Ho Lee

Dept. of Industrial Engineering

Korea Institute of Industrial Technology
Hanyang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	Multimedia Content Analysis and Mining - International Workshop, MCAM 2007, Proceedings
Publisher	Springer Verlag
Pages	184-194
Number of pages	11
ISBN (Print)	9783540734161
DOIs	https://doi.org/10.1007/978-3-540-73417-8_25
State	Published - 2007
Event	International Workshop on Multimedia Content Analysis and Mining, MCAM 2007 - Weihai, China Duration: 30 Jun 2007 → 1 Jul 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4577 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Workshop on Multimedia Content Analysis and Mining, MCAM 2007
Country/Territory	China
City	Weihai
Period	30/06/07 → 1/07/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger

Keywords

Automatic image annotation
Content-based image retrieval
Machine learning
Ontology
Semantic annotation
Support vector machine

Access to Document

10.1007/978-3-540-73417-8_25

Cite this

Jeong, J. W., Park, K. W., Lee, O. S., & Lee, D. H. (2007). Automatic extraction of semantic relationships from images using ontologies and SVM classifiers. In Multimedia Content Analysis and Mining - International Workshop, MCAM 2007, Proceedings (pp. 184-194). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4577 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-73417-8_25

Jeong, Jin Woo ; Park, Kyung Wook ; Lee, Ouk Seh et al. / Automatic extraction of semantic relationships from images using ontologies and SVM classifiers. Multimedia Content Analysis and Mining - International Workshop, MCAM 2007, Proceedings. Springer Verlag, 2007. pp. 184-194 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{1a0188ea4c0c4df393e529161a5a35c0,

title = "Automatic extraction of semantic relationships from images using ontologies and SVM classifiers",

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.",

keywords = "Automatic image annotation, Content-based image retrieval, Machine learning, Ontology, Semantic annotation, Support vector machine",

author = "Jeong, \{Jin Woo\} and Park, \{Kyung Wook\} and Lee, \{Ouk Seh\} and Lee, \{Dong Ho\}",

year = "2007",

doi = "10.1007/978-3-540-73417-8\_25",

language = "English",

isbn = "9783540734161",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "184--194",

booktitle = "Multimedia Content Analysis and Mining - International Workshop, MCAM 2007, Proceedings",

note = "International Workshop on Multimedia Content Analysis and Mining, MCAM 2007 ; Conference date: 30-06-2007 Through 01-07-2007",

}

Jeong, JW, Park, KW, Lee, OS & Lee, DH 2007, Automatic extraction of semantic relationships from images using ontologies and SVM classifiers. in Multimedia Content Analysis and Mining - International Workshop, MCAM 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4577 LNCS, Springer Verlag, pp. 184-194, International Workshop on Multimedia Content Analysis and Mining, MCAM 2007, Weihai, China, 30/06/07. https://doi.org/10.1007/978-3-540-73417-8_25

Automatic extraction of semantic relationships from images using ontologies and SVM classifiers. / Jeong, Jin Woo; Park, Kyung Wook; Lee, Ouk Seh et al.
Multimedia Content Analysis and Mining - International Workshop, MCAM 2007, Proceedings. Springer Verlag, 2007. p. 184-194 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4577 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automatic extraction of semantic relationships from images using ontologies and SVM classifiers

AU - Jeong, Jin Woo

AU - Park, Kyung Wook

AU - Lee, Ouk Seh

AU - Lee, Dong Ho

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

KW - Automatic image annotation

KW - Content-based image retrieval

KW - Machine learning

KW - Ontology

KW - Semantic annotation

KW - Support vector machine

UR - https://www.scopus.com/pages/publications/37249082481

U2 - 10.1007/978-3-540-73417-8_25

DO - 10.1007/978-3-540-73417-8_25

M3 - Conference contribution

AN - SCOPUS:37249082481

SN - 9783540734161

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 184

EP - 194

BT - Multimedia Content Analysis and Mining - International Workshop, MCAM 2007, Proceedings

PB - Springer Verlag

T2 - International Workshop on Multimedia Content Analysis and Mining, MCAM 2007

Y2 - 30 June 2007 through 1 July 2007

ER -

Jeong JW, Park KW, Lee OS, Lee DH. Automatic extraction of semantic relationships from images using ontologies and SVM classifiers. In Multimedia Content Analysis and Mining - International Workshop, MCAM 2007, Proceedings. Springer Verlag. 2007. p. 184-194. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-73417-8_25

Automatic extraction of semantic relationships from images using ontologies and SVM classifiers

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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