Unsupervised segmentation of gray level Markov model textures with hierarchical self organizing maps

Date

1996-01-01

Author

Goktepe, Mesut
Yalabik, Nese
Atalay, Mehmet Volkan

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Segmentation of gray level images into regions of uniform texture is investigated. An unsupervised approach through the use of Kohonen's self organizing map (SOM) and a multilayer version of it, the hierarchical self organizing map (HSOM), is employed to find the regions in an image composed of textures from different classes. For testing, gray level artificial textured images modeled as Markov random fields are used as the input. No parameter estimation is done. The size and the topology of SOM and HSOM are independent from the size of the input image. The segmentation results are very promising. © 1996 IEEE.

Subject Keywords

Self organizing feature maps , Image segmentation , Pixel , Lattices , Markov random fields , Parameter estimation

URI

https://hdl.handle.net/11511/57516

DOI

https://doi.org/10.1109/icpr.1996.547240

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

M. Goktepe, N. Yalabik, and M. V. Atalay, “Unsupervised segmentation of gray level Markov model textures with hierarchical self organizing maps,” 1996, vol. 4, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57516.