Title
Large-Scale Image Phylogeny: Tracing Image Ancestral Relationships
Abstract
Similar to organisms that evolve in biology, a document can change slightly overtime, and each new version may, in turn, generate other versions. Multimedia phylogeny investigates the history and evolutionary process of digital objects and includes finding the causal and ancestral document relationships, source of modifications, and the order and transformations that originally created the set of near duplicates. Multimedia phylogeny has direct applications in security, forensics, and information retrieval. This article explores the phylogeny problem for near-duplicate images in large-scale scenarios and present solutions that have straightforward extension to other media such as videos. Experiments with approximately 2 million test cases (with synthetic and real data) show that the proposed methods automatically build image phylogeny trees from partial information about the near duplicates, improving the efficiency and effectiveness of the whole process, and represent a step forward in determining causal relationships between digital images overtime.
Year
DOI
Venue
2013
10.1109/MMUL.2013.17
MultiMedia, IEEE
Keywords
Field
DocType
image processing,multimedia systems,trees (mathematics),ancestral document relationships,causal document relationships,digital images,digital object evolutionary process,digital object history,image ancestral relationship tracing,image phylogeny trees,large-scale image phylogeny,multimedia phylogeny,near-duplicate images,partial information,process effectiveness improvement,process efficiency improvement,ancestral relationships,image dependencies,image phylogeny,multimedia,multimedia applications,multimedia phylogeny,near-duplicate detection,near-duplicate recognition,near-duplicate search
Information retrieval,Image matching,Computer science,Image processing,Digital image,Theoretical computer science,Human–computer interaction,Test case,Phylogenetics,Tracing,Cognitive neuroscience of visual object recognition
Journal
Volume
Issue
ISSN
20
3
1070-986X
Citations 
PageRank 
References 
12
0.51
9
Authors
3
Name
Order
Citations
PageRank
Zanoni Dias126244.40
Siome Goldenstein261847.43
Anderson Rocha391369.11