Workshop on Detection and Identification of Rare Audiovisual Cues (DIRAC)
In conjunction with ECML-PKDD (http://www.ecmlpkdd2010.org/)
Barcelona, 24 September 2010
Machine learning builds models of the world using training data from the application domain and prior knowledge about the problem. The models are later applied to future data in order to estimate the current state of the world. An implied assumption is that the future is stochastically similar to the past. The approach fails when the system encounters situations that are not anticipated from the past experience. In contrast, successful natural organisms identify new unanticipated stimuli and situations and frequently generate appropriate responses.
This workshop aims to discuss moving the art of machine recognition from the classical signal processing/pattern classification paradigm to human-like information extraction.
This means, among other things, to move from interpretation of all incoming data to reliable rejection of non-informative inputs, from passive acquisition of a single incoming stream to active search for the most relevant information in multiple streams, and from a system optimized for one static environment to autonomous adaptation to new changing environments, thus forming the foundation for a new generation of efficient cognitive information processing technologies.
Aims and Scope
The workshop aims to bring together researchers and students from different disciplines (machine learning, data mining, pattern recognition, computer vision, speech processing, neurophysiology, psychophysics, robotics, …) in order to present and discuss in an informal atmosphere new approaches for identifying and reacting to unexpected events in information-rich environments. To achieve this goal we are soliciting two types of contributions: a) mature research results, and b) interesting preliminary results or stimulating position statements. In addition, the workshop will feature at least one discussion session to allow for a more interactive and engaging experience.
Topics of Interest
The workshop’s topics of interest include (but are not limited to):
• learning from small samples
• unusual/abnormal event detection
• trend analysis
• novelty detection
• classification-based/clustering-based/nearest neighbor based/statistical/information theoretical /spectral/ …
• contextual anomaly detection
• audio-visual perception of humans
• human-computer interaction modeling
• speech processing
• image and video processing
• multimodal processing, fusion and fission
• multimodal indexing, structuring and summarization
• annotation and browsing of multimodal data
• machine learning algorithms and their applications to the topics above
Contributions must be in English and formatted according to the Springer-Verlag LNCS/LNAI guidelines. At the time of submission, the papers must not be under review or accepted for publication elsewhere. Papers will be reviewed by at least two members of the Workshop Committee. Papers will be reviewed on the basis of technical quality, originality, significance, and clarity. All submissions will be handled electronically.
Contributions should be in PDF format and submitted to the following email address: email@example.com
Accepted papers will be presented at the workshop and will appear in the conference proceedings. At least one author of each paper must register for the conference and attend the workshop to present the paper.
• Deadline for submissions: 21 June 2010
• Notification: 12 July 2010
• Early registration:
• Camera ready: 21 July 2010
• Workshop day: 24 September 2010
Jörn Anemüller, Carl von Ossietzky University (Germany)
Daphna Weinshall, Hebrew University of Jerusalem (Israel)
Luc van Gool, K.U. Leuven (Belgium) and ETHZ (Switzerland)
Hynek Hermansky, Johns Hopkins University (USA) and Brno University of Technology (Czech Republic)
Luc De Raedt, K.U. Leuven (Belgium)
• Barbara Caputo, IDIAP, (Switzerland)
• Honza Cernocky, Brno University of Technology (Czech Republic)
• Nicolo Cesa-Bianchi, University of Milan La Statale (Italy)
• Vittorio Ferrari, ETH Zurich (Switzerland)
• Frank Ohl, Leibniz Institute (Germany)
• Francesco Orabona, University of Milan La Statale (Italy)
• Tomas Pajdla, Czech Technical University (Czech Republic)
• Misha Pavel, OHSU (USA)
• Tinne Tuytelaars, K.U.Leuven (Belgium)
• Rufin Vogels, K.U.Leuven (Belgium)
• Stefan Wabnik, Fraunhofer Institute (Germany)