Abstract
Image mining and interpretation is a quite complex process. In this article, we propose to model expert knowledge on objects present in an image through an ontology. This ontology will be used to drive a segmentation process by an evolutionary approach. This method uses a genetic algorithm to find segmentation parameters which allow to identify in the image the objects described by the expert in the ontology. The fitness function of the genetic algorithm uses the ontology to evaluate the segmentation. This approach does not needs examples and enables to reduce the semantic gap between automatic interpretation of images and expert knowledge.
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Keywords
- Genetic Algorithm
- Segmentation Algorithm
- Evolutionary Approach
- Watershed Segmentation
- Automatic Interpretation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Vincent, L., Soille, P.: Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991)
Mueller, M., Segl, K., Kaufmann, H.: Edge- and region-based segmentation technique for the extraction of large, man-madeobjects in high-resolution satellite imagery. Pattern Recognition 37(8), 1619–1628 (2004)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, Reading (1989)
Pignalberi, G., Cucchiara, R., Cinque, L., Levialdi, S.: Tuning range image segmentation by genetic algorithm. EURASIP Journal on Applied Signal Processing 8, 780–790 (2003)
Bhanu, B., Lee, S., Das, S.: Adaptive image segmentation using genetic and hybrid search methods. IEEE Transactions on Aerospace and Electronic Systems 31(4), 1268–1291 (1995)
Song, A., Ciesielski, V.: Fast texture segmentation using genetic programming. IEEE Congress on Evolutionary Computation 3, 2126–2133 (2003)
Feitosa, R.Q., Costa, G.A., Cazes, T.B., B., F.: A genetic approach for the automatic adaptation of segmentation parameters. In: International Conference on Object-based Image Analysis (2006)
Haris, K., Efstradiadis, S.N., Maglaveras, N., Katsaggelos, A.K.: Hybrid image segmentation using watersheds and fast region merging. IEEE Transaction On Image Processing 7(12), 1684–1699 (1998)
Najman, L., Schmitt, M.: Geodesic saliency of watershed contours and hierarchical segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(12), 1163–1173 (1996)
Durand, N., Derivaux, S., Forestier, G., Wemmert, C., Gancarski, P., Boussaid, D., O., Puissant, A.: Ontology-based object recognition for remote sensing image interpretation. In: IEEE International Conference on Tools with Artificial Intelligence, Patras, Greece, pp. 472–479 (2007)
Sheeren, D., Puissant, A., Weber, C., Gancarski, P., Wemmert, C.: Deriving classification rules from multiple sensed urban data with data mining. In: 1rst Workshop of the EARSel Special Interest Group Urban Remote Sensing, Berlin (2006)
Janssen, L., Molenaar, M.: Terrain objects, their dynamics and their monitoring by the integration of gis and remote sensing. 33, 749–758 (1995)
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Forestier, G., Derivaux, S., Wemmert, C., Gançarski, P. (2008). An Evolutionary Approach for Ontology Driven Image Interpretation. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2008. Lecture Notes in Computer Science, vol 4974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78761-7_30
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DOI: https://doi.org/10.1007/978-3-540-78761-7_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78760-0
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