Fast image segmentation based on multi-resolution analysis and wavelets
Pattern Recognition Letters
Radon Transform Orientation Estimation for Rotation Invariant Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Modified K-Means Algorithm for Circular Invariant Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reduced Complexity Rotation Invariant Texture Classification Using a Blind Deconvolution Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture image segmentation using combined features from spatial and spectral distribution
Pattern Recognition Letters
Texture classification using Gabor wavelets based rotation invariant features
Pattern Recognition Letters
Texture classification using Gabor wavelets based rotation invariant features
Pattern Recognition Letters
International Journal of Intelligent Systems Technologies and Applications
Texture classification using invariant ranklet features
Pattern Recognition Letters
Intelligent Processing of Medical Images in the Wavelet Domain
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
Radon representation-based feature descriptor for texture classification
IEEE Transactions on Image Processing
Hierarchical multiple Markov chain model for unsupervised texture segmentation
IEEE Transactions on Image Processing
Steerable weighted median filters
IEEE Transactions on Image Processing
Clothes matching for blind and color blind people
ICCHP'10 Proceedings of the 12th international conference on Computers helping people with special needs
Image Segmentation Based on Bacterial Foraging and FCM Algorithm
International Journal of Swarm Intelligence Research
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We introduce a rotational invariant feature set for texture segmentation and classification, based on an extension of fractal dimension (FD) features. The FD extracts roughness information from images considering all available scales at once. In this work, a single scale is considered at a time so that textures with scale-dependent properties are satisfactorily characterized. Single-scale features are combined with multiple-scale features for a more complete textural representation. Wavelets are employed for the computation of single- and multiple-scale roughness features because of their ability to extract information at different resolutions. Features are extracted in multiple directions using directional wavelets, and the feature vector is finally transformed to a rotational invariant feature vector that retains the texture directional information. An iterative K-means scheme is used for segmentation, and a simplified form of a Bayesian classifier is used for classification. The use of the roughness feature set results in high-quality segmentation performance. Furthermore, it is shown that the roughness feature set exhibits a higher classification rate than other feature vectors presented in this work. The feature set retains the important properties of FD-based features, namely insensitivity to absolute illumination and contrast.