Recognition of objects and regions of interest in digital image processing often relies on texture classification. The source image is divided according to a rectangular grid to form textured regions each of which is characterized by some numerical significant measure called feature. A new approach is introduced that uses the gray-level spatial dependence matrices and the genetic clustering with unknown K algorithms to locate sets of homogeneous regions and enhance the discrimination amongst them. There is no need to select and compute complicated features transforms as the procedure is based on the optimal weighting of the simple basic features. A simulation experiment is performed using the well-known Brodatz textures to demonstrate that the new procedure is able to define well separated clusters according to the principle of strong internal cohesion and high inter-clusters separation.
Unsupervised classification of texture images by gray-level spatial dependence matrices and genetic algorithms / Baragona, R.; Bocci, L.. - (2020), pp. 880-885. (Intervento presentato al convegno SIS 2020 tenutosi a Pisa).
Unsupervised classification of texture images by gray-level spatial dependence matrices and genetic algorithms
Baragona R.;Bocci L.
2020
Abstract
Recognition of objects and regions of interest in digital image processing often relies on texture classification. The source image is divided according to a rectangular grid to form textured regions each of which is characterized by some numerical significant measure called feature. A new approach is introduced that uses the gray-level spatial dependence matrices and the genetic clustering with unknown K algorithms to locate sets of homogeneous regions and enhance the discrimination amongst them. There is no need to select and compute complicated features transforms as the procedure is based on the optimal weighting of the simple basic features. A simulation experiment is performed using the well-known Brodatz textures to demonstrate that the new procedure is able to define well separated clusters according to the principle of strong internal cohesion and high inter-clusters separation.File | Dimensione | Formato | |
---|---|---|---|
Baragona_Bocci-SIS2020.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
249.19 kB
Formato
Adobe PDF
|
249.19 kB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.