Abstract
This paper presents a learning-based method for parameter tuning of object recognition systems and its application to automatic road extraction from high resolution remotely sensed (HRRS) images. Our approach is based on region growing using fast marching level set method (FMLSM), and machine learning for automatically tuning its parameters. FMLSM is used to extract the shape of objects in images. Parameters are introduced into the speed function of the FMLSM to improve flexibility and reflect the variety of images. The parameters are tuned using machine learning and utilizing background knowledge. The primary contribution of our approach is the ability to learn the parameters for a FMLSM model for object extraction. Experimental results on 11 HRRS image datasets, 1024*1024 pixels each with ground resolution of 1.3 meters, demonstrate the validity of the proposed algorithm. We are able to extract the roads without the use of heuristic parameters and other manual intervention.
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References
Zhang, C., Murai, S., Baltsavias, E.: Road network detection by mathematical morphology. In: ISPRS Workshop on 3D Geospatial Data Production: Meeting Applicat, Requirements, Paris, pp. 185–200 (1999)
Amini, J., Sarahjian, M.: Image map simplification by using mathematical morphology. ISPRS Journal of Photogrammetry and Remote Sensing 33 (2000)
Sowmya, A., Singh, S.: Rail: Extracting road segments from aerial images using machine learning. In: Proc. ICML 1999 Workshop on Learning in Vision, pp. 8–19 (1999)
Cohen, L.: On active contour models and balloons. CVGIP Image Understanding 53 (1991)
Baumgartner, A., Hinz, S., Wiedemann, C.: Efficient methods, and interfaces for road tracking. In: Proc. ISPRS-Commision III Symp. Photogrammet. Compu. Vision (PCV 2002), Graz, pp. 28–31 (2002)
Laptev, I., Mayer, H., Lindeberg, T., Eckstein, W., Steger, C., Baumgartner, A.: Automatic extraction of roads from aerial images based on scale space and snakes. Machne Vision Applicat. 12, 23–31 (2000)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision, 321–331 (1988)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. In: ICCV 1995, Cambridge, USA, pp. 694–699 (1995)
Chen, A., Donovan, G., Sowmya, A., Trinder, J.: Inductive clustering: automatic low level segmentation in high resolution images. In: ISPRS Photogrammet. Comput. Vision, Graz, Austria, vol. A, p. 73 (2002)
Agouris, P., Doucette, P., Stefanidis, A.: Spatiospectral cluster analysis of elongated regions in aerial imagery. In: IEEE International Conference on Image Processing(ICIP), vol. 2, pp. 789–792 (2001)
Xiongcai, C., Sowmya, A., Trinder, J.: Learning to recognise roads from high resolution remotely sensed images. In: The 2nd International Conference on Intelligent Sensors. Sensor Networks and Information Processing, Melbourne (2005)
Baumgartner, A., Steger, A., Mayer, C., Eckstein, W., Ebner, H.: Automatic road extraction based on multi-scale, grouping and context. Photogrammet. Eng. Remotely Sensing 65, 777–786 (1999)
McKeown, D., Cochran, S., Ford, S., Mcglone, J., Shufelt, J., Yocum, D.: Fusion of hydice hyperspectral data with panchromatic imagery for cartographic feature extraction. IEEE Trans. Geosci. Remote Sensing 27, 1261–1277 (1999)
Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: a level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 17 (1995)
Zlotnick, A., Carnine, P.: Finding roads seeds in aerial images. In: CVGIP, Image Understanding, vol. 57, pp. 243–260 (1993)
Keaton, T., Brokish, J.: Evolving roads in ikonos multispectral imagery. In: Proceedings of International Conference on Image Processing (2003)
Geman, D., Jedynak, B.: An active testing model for tracking roads in satellite images. IEEE Trans. Pattern Anal. Machine Intell. 18 (1996)
Cohen, L.D., Kimmel, R.: Global minimum for active contour models: A minimal path approach. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 666–673 (1996)
Bhanu, B., Sungkee, L., Ming, J.: Adaptive image segmentation using a genetic algorithm. Systems, Man and Cybernetics, IEEE Transactions on 25, 1543 (1995)
Kohavi, R., John, G.: Wrapper for feature subset selection. Journal of Artificial Intelligence 97, 273–324 (1997)
Pratt, W.: Digital Image Processing. Wiley, Chichester (1991)
Haralick, R.: Statistical and structural approaches to texture. Proc. IEEE 67, 786–804 (1979)
Smola, A.J., Sch, B.: A tutorial on support vector regression. NeuroCOLT2 Technical Report Series (1998)
Wiedemann, C., Heipke, C., Mayer, H., Hamet, O.: Empirical evaluation of automatically extracted road axes. In: CVPR Workshop on Empirical EvaluationMethods in Computer Vision, pp. 172–187 (1998)
Burges, C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, San Francisco (2000)
Telea, A., Vilanova, A.: A robust level-set algorithm for centerline extraction. In: Joint EUROGRAPHICS - IEEE TCVG Symposium on Visualization (2003)
Malladi, R., Sethian, J.: A unified approach to noise removal, image enhancement, and shape recovery. IEEE Trans. on Image Processing 5, 1554–1568 (1996)
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Cai, X., Sowmya, A., Trinder, J. (2006). Learning Parameter Tuning for Object Extraction. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_87
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DOI: https://doi.org/10.1007/11612032_87
Publisher Name: Springer, Berlin, Heidelberg
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