Optimal Mean-Precision Classifier
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Hierarchical multiple Markov chain model for unsupervised texture segmentation
IEEE Transactions on Image Processing
Unsupervised texture segmentation using multiple segmenters strategy
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
A hierarchical texture model for unsupervised segmentation of remotely sensed images
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
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A novel color texture unsupervised segmentation algorithm is presented which processes independently the spectral and spatial information. The algorithm is composed of two parts. The former provides an over-segmentation of the image, such that basic components for each of the textures which are present are extracted. The latter is a region growing algorithm which reduces drastically the number of regions, and provides a region-hierarchical texture clustering. The over-segmentation is achieved by means of a color-based clustering (CBC) followed by a spatial-based clustering (SBC). The SBC, as well as the subsequent growing algorithm, make use of a characterization of the regions based on shape and context. Experimental results are very promising in case of textures which are quite regular.