Efficient and Effective Hierarchical Feature Propagation
Abstract
Many methods have been recently proposed to deal with the large amount of data provided by the new remote sensing technologies. Several of those methods rely on the use of segmented regions. However, a common issue in region-based applications is the definition of the appropriate representation scale of the data, a problem usually addressed by exploiting multiple scales of segmentation. The use of multiple scales, however, raises new challenges related to the definition of effective and efficient mechanisms for extracting features. In this paper, we address the problem of extracting features from a hierarchy by proposing two approaches that exploit the existing relationships among regions at different scales. The H-Propagation propagates any histogram-based low-level descriptors. The BoW-Propagation approach uses the bag-of-visual-word model to propagate features along multiple scales. The proposed methods are very efficient as features need to be extracted only at the base of the hierarchy and yield comparable results to low-level extraction approaches.
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