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Conference papers

Characterizing Images by the Gromov-Hausdorff Distances Between Derived Hierarchies

Abstract : A hierarchy is a series of nested partitions in which a coarser partition results from merging regions of finer ones. Each hierarchy derived from an image provides a particular structural description of the image content, depending upon the criteria for merging neighboring regions. Distinct hierarchies derived from a same image reflect its various facets and the distances between them nicely characterize its content. In this paper the hierarchies are constructed with the versatile stochastic watershed algorithm and their inter-distances are measured with the Gromov-Hausdorff distance. Experiments conducted on images simulated by dead leaves model illustrate the advantages of our approach in terms of learning efficiency and understandability of the results.
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Contributor : Santiago Velasco-Forero Connect in order to contact the contributor
Submitted on : Friday, January 18, 2019 - 3:36:09 PM
Last modification on : Wednesday, November 17, 2021 - 12:27:16 PM


  • HAL Id : hal-01986192, version 1


Amin Fehri, Santiago Velasco-Forero, Fernand Meyer. Characterizing Images by the Gromov-Hausdorff Distances Between Derived Hierarchies. 2018 25th IEEE International Conference on Image Processing (ICIP), Oct 2018, Athens, Greece. ⟨hal-01986192⟩



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