K-means Iterative Fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation

Pattern Recognition

Volumn 35, Issue 9, 2002, Pages 1959-1972

  Clausi, D A ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Binary hierarchical clustering; Co occurrence probabilities; Computer vision; Fisher linear discriminant; Gabor filters; Image segmentation; K means; Texture segmentation

Indexed keywords

ALGORITHMS; FEATURE EXTRACTION; ITERATIVE METHODS;

CLUSTERING ALGORITHMS;

IMAGE SEGMENTATION;

IMAGE PROCESSING;

EID: 0036721824     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(01)00138-8     Document Type: Article

References (28)

1. Some fundamental concepts and synthesis procedures for pattern recognition preprocessors
2. Textural features for image classification
3. A combined Markov random field and wave-packet transform-based approach for image segmentation
4. A Markov random field model-based approach to unsupervised texture segmentation using local and global spatial statistics
5. Unsupervised texture segmentation using Markov random field models
6. Unsupervised texture segmentation using Gabor filters
7. Texture classification and segmentation using wavelet frames
8. Multichannel texture analysis using localized spatial filters
9. Unsupervised texture segmentation using multichannel decomposition and hidden Markov models
10. Analysis of multichannel narrow-band filters for image texture segmentation
11. Texture segmentation using 2-D Gabor elementary functions
12. Optimal Gabor filter for texture segmentation
13. A self-organizing network for hyperellipsoidal clustering (HEC)
14. Filtering for texture classification: A comparative study
15. Unsupervised texture segmentation using tuned filters in Gaborian space
16. Designing Gabor filters for optimal texture separability
17. N-folded symmetries by complex moments in Gabor space and their application to unsupervised texture segmentation
18. Evaluation of texture segmentation algorithms
19. Multiresolution Gauss-Markov random field models for texture segmentation
20. GMRF models and wavelet decomposition for texture segmentation
21. A fast method to determine co-occurrence texture features
22. A new vector quantization clustering algorithm