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This paper addresses the problem of feature selection within classification processes. We present a comparison of a feature subset selection with respect to two boosting methods, Adaboost and ADTboost. In our evaluation, we have focused on three different criteria: the classification error and the efficiency of the process depending on the number of most(More)
Multi-class image classification has made significant advances in recent years through the combination of local and global features. This paper proposes a novel approach called hierarchical conditional random field (HCRF) that explicitly models region adjacency graph and region hierarchy graph structure of an image. This allows to set up a joint and(More)
The conservation and sustainable utilization of global biodiversity necessitates the mapping and assessment of the current status and the risk of loss of biodiversity as well as the continual monitoring of biodiversity. These demands in turn require the reliable identification and comparison of animal and plant species or even subspecies. We have developed(More)
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