Manifold Learning and Spectral Clustering for Image Phylogeny Forests

Author(s)

Oikawa, Marina A. ^[1] ; Dias, Zanoni ^[1] ; Rocha, Anderson de Rezende ^[1] ; Goldenstein, Siome ^[1]

Affiliation

^[1] Univ Estadual Campinas, Inst Comp, BR-13083970 Campinas, SP - Brazil

• 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

Abstract

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