Image segmentation using fuzzy correlation
Information Sciences: an International Journal
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments in colour texture analysis
Pattern Recognition Letters
Texture Segmentation Using Independent Component Analysis of Gabor Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
An LBP-Based Active Contour Algorithm for Unsupervised Texture Segmentation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
A pattern similarity scheme for medical image retrieval
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Automatic grey level thresholding through index of fuzziness and entropy
Pattern Recognition Letters
Multiresolution Gauss-Markov random field models for texture segmentation
IEEE Transactions on Image Processing
Image segmentation by histogram thresholding using fuzzy sets
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
Linguistic description about circular structures of the Mars' surface
Applied Soft Computing
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Texture is an important spatial feature, useful for identifying object or region of interest. In texture analysis the foremost task is to extract texture features, which efficiently embody the information about the textural characteristics of the image. This can be used for the segmentation of different texture images. Texture segmentation is the process of partitioning an image into regions with different textures containing similar group of pixels. This paper, mainly focus on the unsupervised segmentation of color textured images as a prototypical application in computer vision. Here the objective is to group pixels or small image patches such that meaningful regions of identical/similar texture are obtained. This paper presents a new approach for color texture segmentation using Haralick features extracted from color co-occurrence matrices. The originality of this approach is to select the most discriminating color texture features extracted from the color co-occurrence matrices calculated in HSI color space. Fuzzified distance metric is used for achieving texture segmentation.