
doi: 10.1109/mmul.2012.10
handle: 11245/1.379982 , 10220/13737 , 10356/99544
With the proliferation of multimedia data, it has become necessary to secure this content from illegal use, efficiently detect and reconstruct illegal activities from it, and use it as a source of intelligence. Serious challenges arise from the sheer data volume, however. The multimedia research community has developed many exciting solutions for dealing with video footage, images, audio, and other multimedia content over recent years, including knowledge extraction, automatic categorization, and indexing. Although this work forms an excellent foundation for protecting and analyzing multimedia content, challenges remain in the complexity of the targeted material, the lack of structure and metadata, and other application-specific constraints. This special issue provides an overview of current research following this mission. The articles originally appeared at the ACM Multimedia 2010 Workshop on Multimedia in Forensics, Security, and Intelligence (MiFor). The six high-quality contributions cover various approaches in the field, ranging from the visual recognition of faces and tattoos to the discovery of near duplicates and content tampering.
DRNTU::Engineering::Computer science and engineering, 004
DRNTU::Engineering::Computer science and engineering, 004
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 15 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
