Abstract
Multi-scale processing is one of the main issues in the segmentation of natural and man-made structures in real worlds scenes. In this work, we use independent component analysis (ICA) to learn sets of multi-scale features specialized for natural and man-made structures, respectively. Then, we use the learned features to represent images according to a simple linear generative model. Finally, we separate each group of structures by analyzing the error of representation for each set of features. The features learned by ICA reflected both second and higher-order statistical information of each dataset. The average time consumed in the segmentation was 3 milliseconds by image block. The system was validated using scenes from different image databases.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Krishnamachari, S., Chellappa, R.: Delineating buildings by grouping lines with mrfs. IEEE Trans. on Pat. Anal. Mach. Intell. 5(1), 164–168 (1996)
Mayer, H.: Automatic object extraction from aerial imagerya survey focusing on buildings. Computer Vision and Image Understanding 74(2), 138–149 (1999)
Torralba, A., Oliva, A.: Statistics of natural image categories. Network: Comput. Neural Syst. 14, 391–412 (2003)
Kumar, S., Hebert, M.: Man-Made Structure Detection in Natural Images using a Causal Multiscale Random Field. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 119–126 (2003)
Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley and Sons, New York (2001)
Olmos, A., Kingdom, F.A.: McGill Calibrated Colour Image Database (2004), http://tabby.vision.mcgill.ca
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)
van Hateren, J.H., van der Schaaf, A.: Independent component filters of natural images compared with simple cells in primary visual cortex. Proc. R. Soc. Lond. B 265, 359–366 (1998)
Field, D.J.: What is the goal of sensory coding? Neural Computation 6, 559–601 (1994)
Vailaya, A., Jain, A.K., Zhang, H.J.: On image classification: City images vs. landscapes. Pattern Recognition 31, 1921–1936 (1998)
Olshausen, B.A., Field, D.J.: Natural image statistics and efficient coding. Network 7, 333–339 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cavalcante, A., Lucena, F., Barros, A.K., Takeuchi, Y., Ohnishi, N. (2009). Segmentation of Natural and Man-Made Structures by Independent Component Analysis. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-00599-2_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00598-5
Online ISBN: 978-3-642-00599-2
eBook Packages: Computer ScienceComputer Science (R0)