Abstract
We introduce SIA, a framework for annotating images automatically using ontologies. An ontology is constructed holding characteristics from multiple information sources including text descriptions and low-level image features. Image annotation is implemented as a retrieval process by comparing an input (query) image with representative images of all classes. Handling uncertainty in class descriptions is a distinctive feature of SIA. Average Retrieval Rank (AVR) is applied to compute the likelihood of the input image to belong to each one of the ontology classes. Evaluation results of the method are realized using images of 30 dog breeds collected from the Web. The results demonstrated that almost 89% of the test images are correctly annotated (i.e., the method identified their class correctly).
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
Kherfi, M., Ziou, D., Bernardi, A.: Image Retrieval from the World Wide Web: Issues, Techniques, and Systems. ACM Computing Surveys 36(1), 35–67 (2004)
Hanbury, A.: A Aurvey of Methods for Image Annotation. Journal of Visual Languages and Computing 19(5), 617–627 (2008)
Jeon, J., Lavrenko, V., Manmatha, R.: Automatic Image Annotation and Retrieval using Cross-Media Relevance Models. In: Proc. of ACM SIGIR 2003, Toronto, CA, pp. 119–126 (July 2003)
Schreiber, A., Dubbeldam, B., Wielemaker, J., Wielinga, B.: Ontology-Based Photo Annotation. IEEE Intelligent Systems 16(3), 66–74 (2001)
Park, K.W., Jeong, J.W., Lee, D.H.: OLYBIA: Ontology-Based Automatic Image Annotation System Using Semantic Inference Rules. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 485–496. Springer, Heidelberg (2007)
Mezaris, V., Kompatsiaris, J.: MStrintzis: Region-Based Image Retrieval using an Object Ontology and Relevance Feedback. EURASIP Journal on Applied Signal Processing 2004(1), 886–901 (2004)
Manjunath, B., Ohm, J., Vasudevan, V., Yamada, A.: Color and Texture Descriptors. IEEE Trans. on Circuits and Systems for Video Technology 11(1), 703–715 (2001)
Rother, C., Kolmogorov, V., Blake, A.: GrabCut: Interactive Foreground Extraction using Iterated Graph Cuts. ACM Transactions on Graphics (TOG) 23(3), 309–314 (2004)
Lux, M., Chatzichristofis, S.: LIRE: Lucene Image Retrieval: An Extensible Java CBIR Library. In: Proc. of the 16th ACM Intern. Conf. on Multimedia (MM 2008), Vancuver, CA, pp. 1085–1088 (November 2008)
Tsinaraki, C., Polydoros, P., Christodoulakis, S.: Interoperability Support between MPEG-7/21 and OWL in DS-MIRF. IEEE Transactions on Knowledge and Data Engineering 19(2), 219–232 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Koletsis, P., Petrakis, E.G.M. (2010). SIA: Semantic Image Annotation Using Ontologies and Image Content Analysis. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2010. Lecture Notes in Computer Science, vol 6111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13772-3_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-13772-3_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13771-6
Online ISBN: 978-3-642-13772-3
eBook Packages: Computer ScienceComputer Science (R0)