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.
This work was supported by Korea Research Foundation Grant funded by the Korea Government(MOEHRD) (KRF-2006-521-D00457).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Smith, J.R., Chang, S.-F.: VisualSEEK: a fully automated content-based image query system. ACM Multimedia 96
Carson, C., Thomas, M., Belongie, S., Joseph, M., Hellerstein.: Blobworld: A System for Region-Based Image Indexing and Retrieval. Visual Information Systems (1999)
Minka, T.P., Picard, R.W.: Interactive Learning Using a Society of Models. Pattern Recognition’ 97 (1997)
Zhang, Q., Goldman, S.A., Yu, W., Fritts, J.E.: Content-Based Image Retrieval Using Multiple-Instance Learning. In: Proc. Machine Learning’02 (2002)
Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Trans. Circuits Syst. Video Technol (1998)
Fan, J., Gao, Y., Luo, H., Xu, G.: Automatic Image Annotation by Using Concept-Sensitive Salient Objects for Image Content Representation. ACM SIGIR’04 (2004)
Li, B., Goh, K.-S., Chang, E.Y.: Confidence-based Dynamic Ensemble for Image Annotation and Semantics Discovery. In: ACM MM’03 (2003)
Tsai, C.-F., McGarry, K., Tait, J.: Automatic Metadata Annotation of Images via a Two-Level Learning Framework. In: ACM SIGIR’04 workshop on semantic web, pp. 32–42 (2004)
Wu, X.: Color quantization by Dynamic Programming and Principal Analysis. In: TOG’ 92 (1992)
Wordnet2.1, http://wordnet.princeton.edu/
Jang, M., Sohn, J.-C.: Bossam: An Extended Rule Engine for OWL Inferencing
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Jeong, JW., Park, KW., Lee, O., Lee, DH. (2007). Automatic Extraction of Semantic Relationships from Images Using Ontologies and SVM Classifiers. In: Sebe, N., Liu, Y., Zhuang, Y., Huang, T.S. (eds) Multimedia Content Analysis and Mining. MCAM 2007. Lecture Notes in Computer Science, vol 4577. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73417-8_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-73417-8_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73416-1
Online ISBN: 978-3-540-73417-8
eBook Packages: Computer ScienceComputer Science (R0)