Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Texture Feature Fusion for High Resolution Satellite Image Classification
CGIV '05 Proceedings of the International Conference on Computer Graphics, Imaging and Visualization
Feature selection and fusion for texture classification
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Design-based texture feature fusion using Gabor filters and co-occurrence probabilities
IEEE Transactions on Image Processing
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This paper describes a method of unsupervised color texture segmentation by efficiently combining different features obtained from multi-channel and multi-resolution filters. The DWT and DCT features are extracted separately from 3 color bands of the image and then fused together for optimal performance. The features are then ranked according to a selection criteria. We propose a new correlation measure for the task of feature ranking. To select the best combination of features to be used, we use the property of cluster scatter of a selected set of features. Finally, the optimum number of ranked order features are used for segmentation using a Fuzzy C-Means classifier. The performance of the proposed segmentation method is verified using standard benchmark datasets.