196,572 research outputs found
1st ACM Workshop on Multimedia in Forensics - MiFor 09, Co-located with the 2009 ACM International Conference on Multimedia, MM 09: Foreword
No full textProceedings of International Workshop on Multimedia in forensics
No full textIt is our great pleasure to welcome you to the 1st ACM Workshop on Multimedia in Forensics -- MiFor'09.With the proliferation of multimedia data on the web, surveillance cameras in cities, and mobile phones in everyday life we see an enormous growth in multimedia data that needs to be analyzed by forensic investigators. The sheer volume of such datasets makes manual inspection of all data impossible. Tools are needed to support the investigator in their quest for relevant clues and evidence and in their strive towards preventing crime.The multimedia community has developed new solutions for management of large collections of video footage, images, audio and other multimedia content, knowledge extraction and categorization, pattern recognition, indexing and retrieval, searching, browsing and visualization, and modeling and simulation in various domains. Due to the inherent uncertainty and complexity of forensic data, applying those techniques to forensic data is not straightforward. The time is ripe to tailor these results for forensics. Multimedia in forensics is the workshop which target is to join the research topics and the applications.The workshop aims at addressing the multimedia toolbox supporting the forensic process from the prevention of crime, capturing and annotation of the crime scene, the investigation of the data in the lab, up to the presentation of the results in court. It is a first attempt in bringing multimedia tools in to this exciting application field. The target audience consists of researchers working on innovative technology, representatives from companies developing tools, and forensic investigators in various disciplines.Despite the ambitious objective for the workshop and it being the first edition, it attracted a good number of quality submissions fairly distributed among different countries and among the different topics of the workshop. The MiFor09 Technical Program Committee includes the most experienced researchers in the related research fields, and thanks to their indispensable effort we were able to select 11 papers for oral presentation.The workshop schedules four oral sessions, named "Detection and Mining", "Multimedia forensics prototypes", "Forgery and Splicing Detection" and "Tracking". In addition, the program includes a keynote address by Professor Mohan Kankanhalli, a distinguished lecturer in the field
Artistic Visual Storytelling
No full textThis directory contains the necessary files for the Artistic Visual Storytelling task. For a short dataset description, please, read the README.md.
Import note: The Artistic Visual Storytelling dataset can be used only for non-commercial academic research purposes.
If you use this dataset, please cite it as below:
Efthymiou, A.; Rudinac, S.; Kackovic, M.; Worring, M.; Wijnberg, N.M. (2023): Artistic Visual Storytelling. University of Amsterdam / Amsterdam University of Applied Sciences. Dataset. https://doi.org/10.21942/uva.20050970.v2</p
Artistic Visual Storytelling
No full textThis directory contains the necessary files for the Artistic Visual Storytelling task. For a short dataset description, please, read the README.md.
Import note: The Artistic Visual Storytelling dataset can be used only for non-commercial academic research purposes.
If you use this dataset, please cite it as below:
Efthymiou, A.; Rudinac, S.; Kackovic, M.; Worring, M.; Wijnberg, N.M. (2023): Artistic Visual Storytelling. University of Amsterdam / Amsterdam University of Applied Sciences. Dataset. https://doi.org/10.21942/uva.20050970.v1</p
Artistic Visual Storytelling
No full textThis directory contains the necessary files for the Artistic Visual Storytelling task. For a short dataset description, please, read the README.md.
Import note: The Artistic Visual Storytelling dataset can be used only for non-commercial academic research purposes.
If you use this dataset, please cite it as below:
Efthymiou, A.; Rudinac, S.; Kackovic, M.; Worring, M.; Wijnberg, N.M. (2023): Artistic Visual Storytelling. University of Amsterdam / Amsterdam University of Applied Sciences. Dataset. https://doi.org/10.21942/uva.20050970.v2</p
Semantic Annotation for Retrieval of Visual Resources
Full text linkBeeldmateriaal speelt een steeds grotere rol in onze cultuur, maar ook in de wetenschap en in het onderwijs. Zoeken in grote collecties beeldmateriaal blijft echter een moeizaam proces. Het kost een eindgebruiker veel tijd en moeite om juist dat ene beeld te vinden. Daarom zijn er efficiënte zoekmethoden nodig om de groeiende collecties doorzoekbaar te maken en te houden. Laura Hollink onderzoekt de problemen bij het zoeken naar beeldmateriaal en de mogelijke oplossingen daarvoor, in drie uiteenlopende collecties: schilderijen, foto’s van organische cellen en nieuwsuitzendingen.Schreiber, A.T. [Promotor]Wielinga, B.J. [Promotor]Worring, M. [Copromotor
Dr. Duane M. Jackson, Morehouse College, July 2011
No full textThis video is a conversation with Dr. Duane M. Jackson. Dr. Jackson talks about his paper, "Recall and the Serial Position Effect: The Role of Primacy and Recency on Accounting Students' Performance." Jackie Daniel, AUC Woodruff Library, is the interviewer
Artistic Visual Storytelling
No full textThis directory contains the necessary files for the Artistic Visual Storytelling task. For a short dataset description, please, read the README.md. Import note: The Artistic Visual Storytelling dataset can be used only for non-commercial academic research purposes. If you use this dataset, please cite it as below: Efthymiou, A.; Rudinac, S.; Kackovic, M.; Worring, M.; Wijnberg, N.M. (2023): Artistic Visual Storytelling. University of Amsterdam / Amsterdam University of Applied Sciences. Dataset. https://doi.org/10.21942/uva.20050970.v
GIGO, Garbage in, Garbage out: An Urban Garbage Classification Dataset
No full text
Full paper can be found here:
Sukel, M., Rudinac, S., Worring, M. (2023). GIGO, Garbage In, Garbage Out: An Urban Garbage Classification Dataset. In: , et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13833. Springer, Cham. https://doi.org/10.1007/978-3-031-27077-2_41
This paper presents a real-world domain-specific dataset, which facilitates algorithm development and benchmarking on the challenging problem of multimodal classification of urban waste in street-level imagery.
The dataset, which we have named ``GIGO: Garbage in, Garbage out,'' consists of 24.999 images collected over a large geographic area of Amsterdam.
The capturing and annotating of the dataset took more than a year as part of a larger project investigating the potential of sensors for more sustainable and efficient waste collection. Our work aims at helping the cities with increasing populations, and thus more waste on the streets to collect different raw materials in a more sustainable fashion.
The collected data differs from existing benchmarking datasets, introducing unique scientific challenges. In this fine-grained classification dataset, the garbage categories are visually heterogeneous with different sizes, origins, materials, and visual appearance of the objects of interest.
Even though challenging, there is an abundance of urban data available in the geographical area of the collected data. Examples are information about demographics, different neighborhood statistics and information about buildings in the vicinity. This allows for experimentation with multimodal approaches.
Relationships within the dataset, information from demographics of the area, different neighborhood statistics, and information about buildings in the vicinity allows for different approaches to help solve the challenging task.
In addition, we provide several state-of-the-art baselines utilizing the different modalities of the dataset. Furthermore, we give suggestions on what can be done on the dataset, such as transformers to use the images' metadata effectively or graph structures that can process information between several images.
Additional contextual data can be found on maps.amsterdam.nl and data.amsterdam.nl.
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