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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

New dissimilarity measures for image phylogeny reconstruction

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Costa, Filipe [1] ; Oliveira, Alberto [1] ; Ferrara, Pasquale [2] ; Dias, Zanoni [1] ; Goldenstein, Siome [1] ; Rocha, Anderson [1]
Total Authors: 6
[1] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil
[2] Univ Firenze, Florence - Italy
Total Affiliations: 2
Document type: Journal article
Source: PATTERN ANALYSIS AND APPLICATIONS; v. 20, n. 4, p. 1289-1305, NOV 2017.
Web of Science Citations: 2

Image phylogeny is the problem of reconstructing the structure that represents the history of generation of semantically similar images (e.g., near-duplicate images). Typical image phylogeny approaches break the problem into two steps: (1) estimating the dissimilarity between each pair of images and (2) reconstructing the phylogeny structure. Given that the dissimilarity calculation directly impacts the phylogeny reconstruction, in this paper, we propose new approaches to the standard formulation of the dissimilarity measure employed in image phylogeny, aiming at improving the reconstruction of the tree structure that represents the generational relationships between semantically similar images. These new formulations exploit a different method of color adjustment, local gradients to estimate pixel differences and mutual information as a similarity measure. The results obtained with the proposed formulation remarkably outperform the existing counterparts in the literature, allowing a much better analysis of the kinship relationships in a set of images, allowing for more accurate deployment of phylogeny solutions to tackle traitor tracing, copyright enforcement and digital forensics problems. (AU)

FAPESP's process: 13/05815-2 - Image and video phylogeny reconstruction
Grantee:Filipe de Oliveira Costa
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 15/19222-9 - DéjáVu: social media forensics for interpreting criminal events
Grantee:Anderson de Rezende Rocha
Support type: Scholarships abroad - Research