iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/978-3-642-14883-5_7
Natural Scene Segmentation Method through Hierarchical Nature Categorization | SpringerLink
Skip to main content

Natural Scene Segmentation Method through Hierarchical Nature Categorization

  • Conference paper
Distributed Computing and Artificial Intelligence

Abstract

In this paper we present a hierarchical learning method to segment natural colour images combining the perceptual information of three natures: colour, texture, and homogeneity. Human knowledge is incorporated to a hierarchical categorisation process, where each nature features are independently categorised. Final segmentation is achieved through a refinement process using the categorisation information from each segment. Experiments are performed using the Berkeley Segmentation Dataset achieving good results even when comparing them to other significant methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 469.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 599.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Antón-Rodríguez, M., Díaz-Pernas, F.J., Díez-Higuera, J.F., Martínez-Zarzuela, M., González-Ortega, D., Boto-Giralda, D.: Recognition of coloured and textured images through a multi-scale neural architecture with orientational filtering and chromatic diffusion. Neurocomputing 72(16-18), 3713–3725 (2009)

    Article  Google Scholar 

  2. Carpenter, G.A.: Default ARTMAP. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2003), pp. 1396–1401 (2003)

    Google Scholar 

  3. Carpenter, G.A., Grossberg, S., Rosen, D.B.: Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks 4(6), 759–771 (1991)

    Article  Google Scholar 

  4. Chen, J., Pappas, T.N., Mojsilovic, A., Rogowitz, B.E.: Adaptive perceptual color-texture image segmentation. IEEE Trans. on Image Processing 14(10), 1524–1536 (2005)

    Article  Google Scholar 

  5. Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.: Color image segmentation: advances and prospects. Pattern Recognition 34(12), 2259–2281 (2001)

    Article  MATH  Google Scholar 

  6. Grossberg, S., Huang, T.: ARTSCENE: A neural system for natural scene classification. Journal of Vision 9(4), 1–19 (2009)

    Article  Google Scholar 

  7. Grossberg, S., Williamson, J.R.: A self-organizing neural system for learning to recognize textured scenes. Vision Research 39, 1385–1406 (1999)

    Article  Google Scholar 

  8. Hanbury, A., Marcotegui, B.: Morphological segmentation on learned boundaries. Image and Vision Computing 27(4), 480–488 (2009)

    Article  Google Scholar 

  9. Martin, D.R., Fowlkes, C., Malik, J.: Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues. IEEE Transaction on Pattern Analysis and Machine Intelligence 26(5), 530–549 (2004)

    Article  Google Scholar 

  10. Martínez-Zarzuela, M., Díaz-Pernas, F.J., Antón-Rodríguez, M., Díez-Higuera, J.F., González-Ortega, D., Boto-Giralda, D., López-González, F., De la Torre-Díez, I.: Multi-scale Neural Texture Classification using the GPU as a Stream Processing Engine. Machine Vision and Applications (2010), doi:10.1007/s00138-010-0254-3

    Google Scholar 

  11. Yang, et al.: MATLAB Toolboxes and Segmentation Results from the BSDS (2008), http://www.eecs.berkeley.edu/~yang/software/lossy_segmentation/ (last visited: December 2009)

  12. Pyun, K., Lim, J., Won, C.S., Gray, R.M.: Image segmentation using hidden Markov Gauss mixture models. IEEE Trans. on Image Processing 16(7), 1902–1911 (2007)

    Article  MathSciNet  Google Scholar 

  13. The Berkeley Segmentation Dataset and Benchmark, http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/ (last visited December 2009)

  14. VisTex: Vision texture database, Massachusetts Institute of Technology, http://vismod.media.mit.edu/vismod/imagery/VisionTexture/vistex.html (last visited: December 2009)

  15. Yang, A.Y., Wright, J., Ma, Y., Sastry, S.S.: Unsupervised segmentation of natural images via lossy data compression. Computer Vision and Image Understanding 110(2), 212–225 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Díaz-Pernas, F.J. et al. (2010). Natural Scene Segmentation Method through Hierarchical Nature Categorization. In: de Leon F. de Carvalho, A.P., Rodríguez-González, S., De Paz Santana, J.F., Rodríguez, J.M.C. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14883-5_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14883-5_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14882-8

  • Online ISBN: 978-3-642-14883-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics