A histogram semantic-based distance for multiresolution image classification

Camille Kurtz 1 Nicolas Passat 1 Pierre Gançarski 1 Anne Puissant 2
1 LSIIT - Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection
2 LIVE - Laboratoire Image, Ville, Environnement
Abstract : Image classification methods based on histogram analysis generally require to use relevant distances for histogram comparison. In this article, we propose a new distance devoted to compare histograms associated to semantic concepts linked by (dis)similarity correlations. This distance, whose computation relies on a hierarchical strategy, captures the multilevel semantic relations between these concepts. It also inherits from the low complexity properties of standard bin-to-bin distances, thus leading to fast and accurate results in the context of multiresolution image classification. Experiments performed on satellite images emphasize the relevance and usefulness of the proposed distance.
Keywords : histogram distance classification background knowledge multiresolution images remote sensing
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Camille Kurtz, Nicolas Passat, Pierre Gançarski, Anne Puissant. A histogram semantic-based distance for multiresolution image classification. International Conference on Image Processing (ICIP), 2012, Orlando, United States. pp.1157-1160, ⟨10.1109/ICIP.2012.6467070⟩. ⟨hal-01695060⟩

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