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A histogram semantic-based distance for multiresolution image classification

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.
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Submitted on : Thursday, February 15, 2018 - 11:57:33 AM
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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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