Adaptive Feature Selection in Image Segmentation

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Adaptive Feature Selection in Image Segmentation
Most image segmentation algorithms optimize some mathematical similarity criterion derived from several low-level image features. One possible way of combining different types of features, e.g. color- and texture features on different scales and/or different orientations, is to simply stack all the individual measurements into one high-dimensional feature vector. Due to the nature of such stacked vectors, however, only very few components (e.g. those which are defined on a suitable scale) will carry information that is relevant for the actual segmentation task. We present an approach to combining segmentation and adaptive feature selection that overcomes this relevance determination problem. All free model parameters of this method are selected by a resampling-based stability analysis. Experiments demonstrate that the built-in feature selection mechanism leads to stable and meaningful partitions of the images.
Volker Roth, Tilman Lange
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where DAGM
Authors Volker Roth, Tilman Lange
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