Abstract
Knowledge of paintings domain includes a variety of sources such as essays, visual examples, ontologies of artistic concepts and user- provided annotations. This knowledge serves several purposes. First, it defines a wide range of concepts for annotation and flexible retrieval of paintings. Second, it serves to bootstrap auto-annotation and disambiguate the generated candidate labels. Third, the user-provided annotations serve to discover folksonomies of concepts and vernacular terms. In this paper, we propose a framework for paintings auto-annotation that incorporates user provided images and annotations, domain ontology and external knowledge sources. We utilize these sources of information to bootstrap and support the auto-annotation task, which is based on transductive inference mechanism that combines probabilistic clustering and multi-expert approach to generate labels. We further combine user-provided annotations with generated labels and domain ontology to disambiguate the concepts. In our experiments, we focus on the auto-annotation of painting and demonstrate that the user-provided annotations significantly increase annotation accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Canaday, J.: Mainstreams of Modern Art. Saunders College Publishing (1981)
Corridoni, J.M., Del Bimbo, A.: Retrieval of Paintings Using Effects Induced by Color Features. In: CAIVD, pp. 2–11 (1998)
El-Yaniv, R., Gerzon, L.: Effective Transductive Learning via PAC-Bayesian Model Selection. Technical Report CS-2004-05, IIT (2004)
Herik, H.J., van den Postma, E.O.: Discovering the Visual Signature of Painters. Future Directions for Intelligent Systems and Information Sciences , 129–147 (2000)
Itten, J.: The Art of Color, Reinhold Pub. Corp (1961)
Li, J., Wang, J.Z.: Studying Digital Imagery of Ancient Paintings by Mixtures of Stochastic Models. IEEE Trans. on Image Proc 13(3) (2004)
Lin, D.: Using Syntactic Dependency as Local Context to Resolve Word Sense Ambiguity, ACL, pp. 64–71 (1997)
Marchenko, Y., Chua, T.-S., Aristarkhova, I., Jain, R.: Representation and Retrieval of Paintings based on Art History Concepts. IEEE ICME (2004)
Marchenko, Y., Chua, T.-S., Jain, R.: Ontology-Based Annotation of paintings using Transductive Inference Framework. In: Int’l Conf on MultimediaModelling (2007)
Paul Getty Trust. Art and Architecture Thesauri and United List of Artist names (2000)
Punyakanok, V., Roth, D., Yih, W., Zimak, D.: Semantic role labeling via integer linear programming inference. In: Proceedings of International Conference on Computational Linguistics (2004)
Schreiber, A.T., Dubbeldam, B., Wielemaker, J., Wielinga, B.J.: Ontology-based photo annotation. IEEE Intelligent Systems 16, 66–74 (2001)
Tsai, T.-H., Wu, C.-W., Lin, Y.-C., Hsu, W.-L.: Exploiting Full Parsing Information to Label Semantic Roles Using an Ensemble of ME and SVM via Integer Linear Programming. In: The CoNLL-2005 Shared Task on Semantic Role Labeling
Vapnik, V.: Estimation of Dependences Based on Empirical Data. Springer, Heidelberg (1982)
Web Museum. Available at http://www.ibiblio.org/wm/
Wu, S., Flach, P.A.: Feature selection with labelled and unlabelled data. CML/PKDD 2002, pp.156–167, http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=1000659
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Leslie, L.M., Chua, TS., Jain, R. (2007). Auto-Annotation of Paintings Using Social Annotations, Domain Ontology and Transductive Inference. In: Ip, H.HS., Au, O.C., Leung, H., Sun, MT., Ma, WY., Hu, SM. (eds) Advances in Multimedia Information Processing – PCM 2007. PCM 2007. Lecture Notes in Computer Science, vol 4810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77255-2_29
Download citation
DOI: https://doi.org/10.1007/978-3-540-77255-2_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77254-5
Online ISBN: 978-3-540-77255-2
eBook Packages: Computer ScienceComputer Science (R0)