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Image Hierarchical Segmentation Based on a GHSOM

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Neural Information Processing (ICONIP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5863))

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Abstract

A novel approach for image segmentation is proposed in this paper. This approach is based on the growing hierarchical self-organizing map (GHSOM), which consists of a hierarchical architecture composed of growing self-organizing maps (SOMs). The SOMs have shown to be successful for the analysis of high-dimensional input data as in data mining applications. The hierarchical architecture of the GHSOM is more flexible than a single SOM in the adaptation process to input data, mirroring inherent hierarchical relations among input data. Image hierarchical segmentation can be achieved by using this neural network model, where the hierarchical structure of segmented regions is captured. In order to evaluate the performance of this segmentation method, an application for hierarchical background modeling in video sequences is provided. Therefore, foreground detection is achieved. Experimental results show that the proposed approach is promising for applications where hierarchical segmentation is required.

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References

  1. Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological cybernetics 43, 59–69 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  2. Rauber, A., Merkl, D., Dittenbach, M.: The growing hierarchical self-organizing map: Exploratory analysis of high-dimensional data. IEEE Transactions on Neural Networks 13, 1331–1341 (2002)

    Article  Google Scholar 

  3. Beaulieu, J., Goldberg, M.: Hierarchy in picture segmentation: a stepwise optimization approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 150–163 (1989)

    Article  Google Scholar 

  4. Fan, J., Yau, D., Elmagarmid, A., Aref, W.: Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Transactions on Image Processing 10, 1454–1466 (2001)

    Article  MATH  Google Scholar 

  5. Ohkura, K., Nishizawa, H., Obi, T., Hasegawa, A., Yamaguchi, M., Ohyama, N.: Unsupervised image segmentation using hierarchical clustering. Optical Review (2000)

    Google Scholar 

  6. Alahakoon, D., Halgamuge, S., Srinivasan, B.: Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Transactions on Neural Networks 11, 601–614 (2000)

    Article  Google Scholar 

  7. Dittenbach, M., Rauber, A., Merkl, D.: Recent advances with the growing hierarchical self-organizing map. In: 3rd Workshop on Self-Organising Maps (WSOM), pp. 140–145 (2001)

    Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Palomo, E.J., Domínguez, E., Luque, R.M., Muñoz, J. (2009). Image Hierarchical Segmentation Based on a GHSOM. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_85

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  • DOI: https://doi.org/10.1007/978-3-642-10677-4_85

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10676-7

  • Online ISBN: 978-3-642-10677-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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