Abstract
The problem of aerial image segmentation using Rough sets and neural networks has been considered. Integrating the advantages of two approaches, this paper presents a hybrid system different from those previous works where rough sets were used only for accelerating or simplifying the process of using neural networks for aerial image segmentation. The hybrid system have been advanced to improve its performance or to explore new structures. These new segmentation algorithms avoids the difficulty of extracting rules from a trained neural network and possesses the robustness which are lacking for rough set based approaches. The proposed schemes are tested comparatively on a bank of test images as well as real world images.
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
Bowyer, K.W., Phillips, P.J.: Empirical Evaluation Techniques in Computer Vision. Wiley-IEEE Computer Society Press (1998)
Erdem, C.E., Sanker, B., Tekalp, A.M.: Performance Measures for Video Object Segmentation and Tracking. IEEE Transactions on Image Processing 13, 937–951 (2004)
Gelasca, E.D., Ebrahimi, T., Farias, M., Carli, M., Mitra, S.: Towards Perceptually Driven Segmentation Evaluation Metrics. In: Proceedings of Conference on Computer Vision and Pattern Recognition Workshop (CVPRW 2004), vol. 4 (2004)
Pichel, J.C., Singh, D.E., Rivera, F.F.: Image Segmentation Based on Merging of Sub-optimal Segmentations. Pattern Recognition Letters 10 (2006)
Pawlak, Z.: Rough sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishing, Dordrecht (1991)
Yeh, C.C., Chi, D.J., Hsu, M.F.: A Hybrid Approach of DEA, Rough set and Support Vector Machines for Business Failure Prediction. Expert Systems with Applications (2009)
Pawlak, Z.: Rough Set and Intelligent Data Analysis. Information Science 11, 1–12 (2002)
Basheer, I.A., Hajmeer, M.: Artificial Neural Networks: Fundamentals, Computing, Design, and Application. Journal of Microbiological Methods 43, 3–31 (2002)
Van Droogenbroeck, M., Barnich, O.: Design of Statistical Measures for the Assessment of Image Segmentation Schemes. In: Proceedings of International Conference on Computer Analysis of Images and Patterns (2005)
Ge, F., Wang, S., Liu, T.: Image-Segmentation Evaluation from the Perspective of Salient Object Extraction. In: Proceedings of IEEE Internatioanl Conference on Computer Vision and Pattern Recognition, vol. I, pp. 1146–1153 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fu, X., Liu, J., Wang, H., Zhang, B., Gao, R. (2012). Rough Sets and Neural Networks Based Aerial Images Segmentation Method. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-34478-7_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34477-0
Online ISBN: 978-3-642-34478-7
eBook Packages: Computer ScienceComputer Science (R0)