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Automatic feature selection for unsupervised image segmentation
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DOI：10.1063/1.1289267.png)
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Abstract
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A computational bottleneck is often imposed by the volume of image data generated in disciplines such as remote sensing and medical imaging, especially in situations where automatic analysis or interpretation is required. Segmentation and classification tasks that utilize multivariate data can be impeded by this dimensionality. A general-purpose unsupervised image segmentation system is presented here for the automatic detection of image regions exhibiting different visual texture properties. A suboptimal feature selection procedure is proposed to automatically select the set of texture features best suited for the particular application. Results are presented for the segmentation of ground-penetrating radar images for generating automatic subsurface reports. The reduction in the size of the feature set both reduces the computation time and improves the accuracy. (C) 2000 American Institute of Physics. [S0003-6951(00)01034-2].
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