Abstract
To date, automatic recognition of semantic information such as salient objects and mid-level concepts from images is a challenging task. Since real-world objects tend to exist in a context within their environment, the computer vision researchers have increasingly incorporated contextual information for improving object recognition. In this paper, we present a method to build a visual contextual ontology from salient objects descriptions for image annotation. The ontologies include not only partOf/kindOf relations, but also spatial and co-occurrence relations. A two-step image annotation algorithm is also proposed based on ontology relations and probabilistic inference. Different from most of the existing work, we exploit how to combine representation of ontology, contextual knowledge and probabilistic inference. The experiments in the LabelMe dataset show that image annotation results are improved using contextual knowledge.
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.
Similar content being viewed by others
References
Ge, F., Wang, S., Liu, T.: Image Segmentation Evaluation From the Perspective of Salient Object Extraction. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), New York, USA (2006)
Fan, J., Gao, Y., Luo, H., Xu, G.: Salient Objects: Semantic Building Blocks for Image Concept Interpretation. In: 3rd ACM International Conference on Image and Video Retrieval (2004)
Heitz, G., Koller, D.: Learning Spatial Context: Using Stuff to Find Things. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 30–43. Springer, Heidelberg (2008)
Rabinovich, A., Vedaldi, A., Galleguillos, C., Wiewiora, E., et al.: Objects in Context. In: IEEE International Conference on Computer Vision (ICCV), Janeiro, Brazil (2007)
Tu, Z.: Auto-context and its application to high-level vision tasks. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska (2008)
Wolf, L., Bileschi, S.: A Critical View of Context. International Journal of Computer Vision 69(2), 251–261 (2006)
Galleguillos, C., Rabinovich, A., Belongie, S.: Object Categorization using Co-Occurrence, Location and Appearance. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska (2008)
Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a Database and Web-based Tool for Image Annotation. International Journal of Computer Vision 77(1-3), 157–173 (2008)
Gao, Y., Fan, J.: Incorporating concept ontology to enable probabilistic concept reasoning for multi-level image annotation. In: 8th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 79–88 (2006)
Spyrou, E., Mylonas, P., Avrithis, Y.: Using region semantics and visual context for scene classification. In: Proceedings of 15th International Conference on Image Processing (ICIP 2008), pp. 53–56 (2008)
Mylonas, P., Spyrou, E., Avrithis, Y.: Enriching a context ontology with mid-level features for semantic multimedia analysis. In: 1st Workshop on Multimedia Annotation and Retrieval enabled by Shared Ontologies, co-located with SAMT 2007 (2007)
Liu, Y., Zhang, J., Tjondronegor, D., Geva, S., et al.: An Improved Image Segmentation Algorithm for Salient Object Detection. In: The 23rd International Conference on Image and Vision Computing New Zealand (IVCNZ 2008), pp. 1–6 (2008)
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
Liu, Y., Zhang, J., Tjondronegoro, D., Geva, S., Li, Z. (2010). Mid-Level Concept Learning with Visual Contextual Ontologies and Probabilistic Inference for Image Annotation. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, YP.P. (eds) Advances in Multimedia Modeling. MMM 2010. Lecture Notes in Computer Science, vol 5916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11301-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-11301-7_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11300-0
Online ISBN: 978-3-642-11301-7
eBook Packages: Computer ScienceComputer Science (R0)