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 -
