Summary
Due to the importance of semantic meaning in image retrieval, manual or semi-automated annotation still remains indispensable in both professional and personal retrieval applications. Annotations are used to facilitate textual or conceptual queries in large image repositories and thus to classify the image data into semantic classes. However, different users’ perception of image contents and the lack of standards among different annotation tools make it necessary to develop methods for the unification and integration of different annotation schemes. In this chapter we present a graph approach as a representation technique for the complex semantic annotation space which is generated by the transformation of the subjective perceptions into a unified knowledge base. Our technique bridges the discrepancy between users’ vocabulary and the several levels of abstraction at which content descriptions are assigned. Based on examples, we show how to integrate our method into probabilistic approaches to (semi-) automatic image annotation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Rong Zhao and William I. Grosky. Bridging the Semantic Gap in Image Retrieval. In Distributed Multimedia Databases: Techniques & Applications, pages 14–36, Hershey, PA, USA, 2002. Idea Group Publishing.
T. Huang, Y. Rui, M. Ortega, and S. Mehrotra. Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval. IEEE Transactions on Circuits and Systems for Video Technology, pages 25–36, 1998.
Y. Rui, T. Huang, and S. Mehrotra. Relevance Feedback Techniques in Interactive Content-Based Image Retrieval. In Storage and Retrieval for Image and Video Databases (SPIE), pages 25–36, 1998.
Y. Rui, T. Huang, and S. Mehrotra. Content-Based Image Retrieval with Relevance Feedback in MARS. In Proceedings of the 1997 International Conference on Image Processing (ICIP ’97), pages 815–818, 1997.
Wayne Niblack, Ron Barber, William Equitz, Myron Flickner, Eduardo H. Glasman, et al. QBIC Project: Querying Images by Content, using Color, Texture, and Shape. In Proceedings of Storage and Retrieval for Image and Video Databases (SPIE), volume 1908, April 1993.
Pu-Jen Cheng and Lee-Feng Chien. Effective Image Annotation for Search using Multi-level Semantics. In Proceedings of International Conference of Asian Digital Libraries, pages 230–242. Springer, 2003.
L. Wenyin, S. Dumais, Y. Sun, H. Zhang, M. Czerwinski, and B. Field. Semi-Automatic Image Annotation. In Proceedings International Conference on Human–Computer Interaction (INTERACT’01), pages 326–333, 2001.
P. Duygulu, Kobus Barnard, J. F. G. de Freitas, and David A. Forsyth. Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary. In ECCV ’02: Proceedings of the 7th European Conference on Computer Vision-Part IV, pages 97–112, London, UK, 2002. Springer, Berlin Heidelberg New York.
Jose Torres, Alan Parkes, and Luis Corte-Real. Region-Based Relevance Feedback in Concept-Based Image Retrieval. In Proceedings of the 5th International Workshop on Image Analysis for Multimedia Interactive Services, Lisboa, Portugal, 2004.
L. Hollink, G. Schreiber, J. Wielemaker, and B. Wielinga. Semantic Annotation of Image Collections. In Proceedings of the K-CAP 2003 Workshop on Knowledge Markup and Semantic Annotation, 2003.
A. Th. Schreiber, Barbara Dubbeldam, Jan Wielemaker, and Bob Wielinga. Ontology-Based Photo Annotation. IEEE Intelligent Systems, 16(3):66–74, 2001.
Rosalind W. Picard, Thomas P. Minka, and Martin Szummer. Modeling User Subjectivity in Image Libraries. In IEEE International Conference On Image Processing, volume 2, pages 777–780, Lausanne, Switzerland, 1996.
Micheline Beaulieu, Pia Borlund, Peter Brusilovsky, et al. Matthew Chalmers. Personalisation and Recommender Systems in Digital Libraries. Joint NSF-EU DELOS Working Group Report. Technical Report, May 2003.
Masashi Inoue. On the Need for Annotation-based Image Retrieval. In Workshop on Information Retrieval in Context (IRiX), pages 44–46, Sheffield, UK, 2004.
James Griffioen, Rajiv Mehrotra, and Rajendra Yavatkar. An Object-Oriented Model for Image Information Representation. In CIKM ’93: Proceedings of the Second International Conference on Information and Knowledge Management, pages 393–402, New York, NY, USA, 1993. ACM Press.
Rosalind W. Picard and Thomas P. Minka. Vision Texture for Annotation. In Multimedia Systems, volume 3, pages 3–14, 1995.
Takio Kurita and Toshikazu Kato. Learning of Personal Visual Impression for Image Database Systems. In Second International Conference on Document Analysis and Recognition, pages 547–552, 1993.
Joo-Hwee Lim. Building Visual Vocabulary for Image Indexation and Query Formulation. In Pattern Analysis and Applications (Special Issue on Image Indexation), volume 4, pages 125–139, 2001.
Joo-Hwee Lim, Qi Tian, and Philippe Mulhem. Home Photo Content Modeling for Personalized Event-Based Retrieval. IEEE MultiMedia, 10(4):28–37, 2003.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Vompras, J., Conrad, S. (2008). Management and Processing of Personalized Annotations in Image Retrieval Systems. In: Wallace, M., Angelides, M.C., Mylonas, P. (eds) Advances in Semantic Media Adaptation and Personalization. Studies in Computational Intelligence, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76361_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-76361_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76359-8
Online ISBN: 978-3-540-76361-1
eBook Packages: EngineeringEngineering (R0)