Optimal filtering for unsupervised texture feature extraction
Trygve Randen, Vidar Alvestad, and John Håkon Husøy
Høgskolen i Stavanger
P.O. Box 2557 Ullandhaug, N-4004 Stavanger, Norway
In Proc. SPIE Visual Communication and Image Processing,
Orlando, FL, March 1996, pp. 441-452
In this paper a technique for unsupervised optimal feature
extraction and segmentation for textured images is presented.
The image is first divided into cells of equal size, and similarity
measures on the autocorrelation functions for the cells are estimated.
The similarity measures are used for clustering the image into clusters
of cells with similar textures.
Autocorrelation estimates for each cluster are then estimated, and
two-dimensional texture feature extractors using filters, optimal with
respect to the Fisher criterion, are constructed.
Further, a model for the feature response at and near the texture
borders is developed.
This model is used to estimate whether the positions of the detected
edges in the image are biased, and a scheme for correcting such bias
using morphological dilation is devised.
The article is concluded with experimental results for the proposed
unsupervised texture segmentation scheme.