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Communication Dans Un Congrès Année : 2018

Image Reassembly Combining Deep Learning and Shortest Path Problem

Résumé

This paper addresses the problem of reassembling images from disjointed fragments. More specifically, given an unordered set of fragments, we aim at reassembling one or several possibly incomplete images. The main contributions of this work are: 1) several deep neural architectures to predict the relative position of image fragments that out-perform the previous state of the art; 2) casting the reassembly problem into the shortest path in a graph problem for which we provide several construction algorithms depending on available information; 3) a new dataset of images taken from the Metropolitan Museum of Art (MET) dedicated to image reassembly for which we provide a clear setup and a strong baseline.
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Dates et versions

hal-01869765 , version 1 (06-09-2018)
hal-01869765 , version 2 (04-12-2018)

Identifiants

  • HAL Id : hal-01869765 , version 2

Citer

Marie-Morgane Paumard, David Picard, Hedi Tabia. Image Reassembly Combining Deep Learning and Shortest Path Problem. European Conference on Computer Vision (ECCV 2018), Sep 2018, Munich, Germany. ⟨hal-01869765v2⟩
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