Mixture Models and the Segmentation of Multimodal Textures
نویسنده
چکیده
A problem of using mixture-of-Gaussian models for unsupervised texture segmentation is that “multimodal” textures (such as can often be encountered in natural images) cannot be well represented by a single Gaussian cluster. We propose a divide-andconquer method that groups together Gaussian clusters (estimated via Expectation Maximization) into homogeneous texture classes. This method allows to succesfully segment even rather complex textures, as demonstrated by experimental tests on natural images.
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