Algorithms for clustering data
Algorithms for clustering data
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Unsupervised Multiscale Image Segmentation
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
A framework for texture analysis based on spatial filtering
A framework for texture analysis based on spatial filtering
Efficient graph-based energy minimization methods in computer vision
Efficient graph-based energy minimization methods in computer vision
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Entropy controlled gauss-markov random measure field models for early vision
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
Automated performance evaluation of range image segmentation algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Design-based texture feature fusion using Gabor filters and co-occurrence probabilities
IEEE Transactions on Image Processing
Independent component analysis of Gabor features for face recognition
IEEE Transactions on Neural Networks
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We present an algorithm for automatic selection of features that best segment an image in texture homogeneous regions. The set of “best extractors” are automatically selected among the Gabor filters, Co-occurrence matrix, Law's energies and intensity response. Noise-features elimination is performed by taking into account the magnitude and the granularity of each feature image, i.e. the compute image when a specific feature extractor is applied. Redundant features are merged by means of probabilistic rules that measure the similarity between a pair of image feature. Then, cascade applications of general purpose image segmentation algorithms (K-Means, Graph-Cut and EC-GMMF) are used for computing the final segmented image. Additionally, we propose an evolutive gradient descent scheme for training the method parameters for a benchmark image set. We demonstrate by experimental comparisons, with stat of the art methods, a superior performance of our technique.