Title :
Unsupervised texture segmentation based on histogram of encoded Gabor features and MRF model
Author :
Pok, Gouchol ; Liu, Jyh-Cham
Author_Institution :
Dept. of Comput. Sci., Texas A&M Univ., College Station, TX, USA
Abstract :
In this paper, we propose an unsupervised texture segmentation scheme in which Gabor transforms and GMRF model are integrated. The Gabor filters are used to extract low-level textural features. The Gabor feature vectors are mapped to an 1-D space using the Kohnen´s SOFM algorithm, and then encoded by the feature map indices. The histogram of encoded features over a small window are used to determine the regions of homogeneous textures. From these regions, class-specific parameters for GMRF model are estimated and used to detect exact boundaries of different textures
Keywords :
image segmentation; image texture; self-organising feature maps; unsupervised learning; Kohnen´s SOFM; MRF model; encoded Gabor features; texture segmentation; unsupervised; Application software; Biomedical imaging; Clustering algorithms; Computer applications; Computer science; Feature extraction; Frequency; Gabor filters; Histograms; Image segmentation;
Conference_Titel :
Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on
Conference_Location :
Kobe
Print_ISBN :
0-7803-5467-2
DOI :
10.1109/ICIP.1999.817102