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

Manifold Learning and Spectral Clustering for Image Phylogeny Forests

Full text
Oikawa, Marina A. [1] ; Dias, Zanoni [1] ; Rocha, Anderson de Rezende [1] ; Goldenstein, Siome [1]
Total Authors: 4
[1] Univ Estadual Campinas, Inst Comp, BR-13083970 Campinas, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: IEEE Transactions on Information Forensics and Security; v. 11, n. 1, p. 5-18, JAN 2016.
Web of Science Citations: 16

The ever-increasing number of gadgets being used to create digital content, as well as the easiness in sharing, editing, and republishing this content, brings the problem of dealing with a large amount of digital objects (e.g., images or videos) whose content is very similar. Some issues faced by investigators of digital crimes when analyzing this type of data include finding the original source of a suspect image, and the responsible for first publishing it. It is also challenging to determine how these objects are related to each other. Recent efforts in developing algorithms to find automatically the underlying relationship among groups of digital media objects with similar content have been explored in the multimedia phylogeny field. A tree structure is used to represent the relationship among these objects, inspired by the phylogenetic trees in biology. Discovering whether these objects came from the same source or from different sources is fundamentally a clustering problem: 1) related objects belong to the same cluster (tree) and 2) unrelated objects should fit in different clusters. In this paper, we address the problem of finding these clusters in sets of semantically similar images, prior to tree reconstruction. We propose the combination of manifold learning and spectral clustering approaches, which have been successfully used in different applications embedding the original data into a lower, but meaningful, dimensional space. Experiments with more than 40 000 test cases show that the proposed approach improves the accuracy in finding the correct number of trees in the set, as well as the reconstruction of the phylogeny trees. (AU)

FAPESP's process: 14/19401-8 - Genome rearrangement algorithms
Grantee:Zanoni Dias
Support type: Regular Research Grants
FAPESP's process: 14/03535-5 - Multimedia Phylogeny Forest Reconstruction: Recovering the ancestry relationship of Images, Videos and Text Documents
Grantee:Marina Atsumi Oikawa
Support type: Scholarships in Brazil - Post-Doctorate