Title :
Unsupervised image segmentation based on a self-organizing feature map and a texture measure
Author_Institution :
Lab. of Inf. & Comput. Sci., Helsinki Univ. of Technol., Espoo, Finland
fDate :
30 Aug-3 Sep 1992
Abstract :
A new approach to unsupervised texture segmentation is represented. The method is based on a local texture measure, a grey tone spatial dependence matrix. The randomly sampled local measures self-organize to a topological feature map. The topological feature map is used as a set of reference vectors later on when the whole image is processed in raster scan manner by the local texture measurement. The label of a region is the address on the topological feature map. The interpretation of a label is given by identified samples. The method has been applied in segmentation of remote sensing images and aerial photographs
Keywords :
image segmentation; image texture; remote sensing; self-adjusting systems; topology; aerial photographs; grey tone spatial dependence matrix; image processing; raster scan; remote sensing images; self-organizing feature map; texture measure; topological feature map; unsupervised texture segmentation; Computer science; Computer vision; Image edge detection; Image segmentation; Image texture analysis; Laboratories; Neurons; Prototypes; Remote sensing; Vector quantization;
Conference_Titel :
Pattern Recognition, 1992. Vol.III. Conference C: Image, Speech and Signal Analysis, Proceedings., 11th IAPR International Conference on
Conference_Location :
The Hague
Print_ISBN :
0-8186-2920-7
DOI :
10.1109/ICPR.1992.201937