|Title||Feature fusion for image texture segmentation|
|Publication Type||Conference Paper|
|Year of Publication||2004|
|Authors||Clausi, D. A., and H. Deng|
|Conference Name||17th International Conference on Pattern Recognition (ICPR)|
|Keywords||additive noise, design based method, Feature Extraction, feature fusion, feature reduction method, Gabor filter, grey level cooccurrence probability, image segmentation, image texture, image texture segmentation, probability, texture analysis, texture recognition, unsupervised image segmentation|
A design-based method to fuse Gabor filter and grey level co-occurrence probability (GLCP) features for improved texture recognition is presented. Feature space separability and unsupervised image segmentation are used for testing. The fused features are robust with respect to the curse of dimensionality and additive noise. Feature reduction methods are typically detrimental to the segmentation performance. Overall, the fused features are a definite improvement over non-fused features and are advocated in texture analysis applications.
Feature fusion for image texture segmentation