A GENERIC CONCEPT FOR OBJECT-BASED IMAGE ANALYSIS

André Homeyer, Michael Schwier, Horst K. Hahn

Abstract

Object-based image analysis enables the recognition of complex image structures that are intractable to conventional pixel-based methods. To date, there is no generally accepted approach for the object-based processing of images, thus making it difficult to transfer developments. In this paper, we propose a generic concept for object-based image analysis that is broadly applicable and founded on established methodologies, such as the attributed relational graph, the relational data model and statistical classifiers. We also describe a reference implementation of the concept as part of the MeVisLab image processing platform.

References

  1. Aksoy, S. (2006). Modeling of remote sensing image content using attributed relational graphs. Lecture Notes in Computer Science, 4109:475-483.
  2. Breiman, L. (2001). Random forests. Machine Learning, 45(1):5-32.
  3. Chang, N.-S. and Fu, K.-S. (1979). A relational database system for images. In Pictorial Information Systems, pages 288-321. Springer.
  4. Guttman, A. (1984). R-trees: A dynamic index structure for spatial searching. In Proceedings of the 1984 ACM SIGMOD international conference on Management of data, pages 47-57. ACM New York, NY, USA.
  5. Hay, G. J. and Castilla, G. (2006). Object-based image analysis: strengths, weaknesses, opportunities and threats (swot). In Lang, S., Blaschke, T., and Schöpfer, E., editors, 1st International Conference on Object-based Image Analysis (OBIA 2006).
  6. Jain, A., Duin, R., and Mao, J. (2000). Statistical pattern recognition: A review. IEEE Transactions on pattern analysis and machine intelligence, pages 4-37.
  7. MeVisLab (2010). Medical image processing and visualization. http://www.mevislab.de. Retrieved February 24, 2010.
  8. Ojala, T., Pietikäinen, M., and Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 971-987.
  9. Schäpe, A., Urbani, M., Leiderer, R., and Athelogou, M. (2003). Fraktal hierarchische, prozeß-und objektbasierte Bildanalyse. Procs BVM, pages 206-210.
  10. Shackelford, A. and Davis, C. (2003). A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas. IEEE Transactions on Geoscience and Remote Sensing, 41(10):2354-2363.
  11. Vincent, L. and Soille, P. (1991). Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6):583-598.
Download


Paper Citation


in Harvard Style

Homeyer A., Schwier M. and K. Hahn H. (2010). A GENERIC CONCEPT FOR OBJECT-BASED IMAGE ANALYSIS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 530-533. DOI: 10.5220/0002848105300533


in Bibtex Style

@conference{visapp10,
author={André Homeyer and Michael Schwier and Horst K. Hahn},
title={A GENERIC CONCEPT FOR OBJECT-BASED IMAGE ANALYSIS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={530-533},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002848105300533},
isbn={978-989-674-029-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - A GENERIC CONCEPT FOR OBJECT-BASED IMAGE ANALYSIS
SN - 978-989-674-029-0
AU - Homeyer A.
AU - Schwier M.
AU - K. Hahn H.
PY - 2010
SP - 530
EP - 533
DO - 10.5220/0002848105300533