Unsupervised segmentation of textured images using a hierarchical neural structure

Access Full Text

Unsupervised segmentation of textured images using a hierarchical neural structure

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© IEE
Published

A hierarchical learning structure, combining a randomly-placed local window, a self-organising map and a local-voting scheme, has been developed for the unsupervised segmentation of textured images, which are modelled by Markov random fields. The system learns to progressively estimate model parameters, and hence classify the various textured regions. A globally correct segregation has consistently been obtained during extensive experiments on both synthetic and natural textured images.

Inspec keywords: self-organising feature maps; image segmentation; neural nets; hierarchical systems; Markov processes; unsupervised learning; image texture

Other keywords: hierarchical learning structure; unsupervised segmentation; model parameter estimation; hierarchical neural structure; local-voting scheme; randomly-placed local window; globally correct segregation; self-organising map; classification; Markov random fields; textured images

Subjects: Pattern recognition; Optical information, image and video signal processing; Neural nets (theory); Other topics in statistics; Other topics in statistics

References

    1. 1)
      • Lampinen, J., Oja, E.: `Self-organizing maps for spatial and temporal AR models', Proc. 6th Scandinavian Conf. on Image Analysis, 1989, p. 120–127.
    2. 2)
      • S. Lakshmanan , H. Derin . Simultaneous parameter estimation and segmentation of Gibbs random fieldsusing simulated annealing. IEEE Trans. , 8 , 799 - 813
    3. 3)
      • P. Brodatz . (1966) Textures: A photographic album for artists and designees.
    4. 4)
      • B.S. Manjunath , R. Chellappa . Unsupervised texture segmentation using Markov random field models. IEEE Trans. , 5 , 478 - 482
    5. 5)
      • S. Geman , D. Geman . Stochastic relaxation, Gibbs distributions, and the Bayesian restorationof images. IEEE Trans. , 6 , 721 - 741
    6. 6)
      • T. Kohonen . (1984) Self-organization and associative memory.
    7. 7)
      • Yin, H., Allinson, N.M.: `Self-organised segmentation for textured images', Proc. ICANN'94, 1994, p. 1149–1152.
http://iet.metastore.ingenta.com/content/journals/10.1049/el_19941275
Loading

Related content

content/journals/10.1049/el_19941275
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading