Dependable Semantic Inference

Call for Papers

After many years of exciting research, the field of multimedia information retrieval (MIR) has become mature enough to enter a new development phase — the phase in which MIR technology is made ready to get adopted in practical solutions and realistic application scenarios. High users' expectations in such scenarios require high dependability of MIR systems. For example, in view of the paradigm “getting the content I like, anytime and anyplace” the service of consumer-oriented MIR solutions (e.g., a PVR, mobile video, music retrieval, web search) will need to be at least as dependable as turning a TV set on and off. Dependability plays even a more critical role in automated surveillance solutions relying on MIR technology to analyze recorded scenes and events and alert the authorities when necessary.

This special issue addresses the dependability of those critical parts of MIR systems dealing with semantic inference. Semantic inference stands for the theories and algorithms designed to relate multimedia data to semantic-level descriptors to allow content-based search, retrieval, and management of data. An increase in semantic inference dependability could be achieved in several ways. For instance, better understanding of the processes underlying semantic concept detection could help forecast, prevent, or correct possible semantic inference errors. Furthermore, the theory of using redundancy for building reliable structures from less reliable components could be applied to integrate “isolated” semantic inference algorithms into a network characterized by distributed and collaborative intelligence (e.g., a social/P2P network) and let them benefit from the processes taking place in such a network (e.g., tagging, collaborative filtering).

The goal of this special issue is to gather high-quality and original contributions that reach beyond conventional ideas and approaches and make substantial steps towards dependable, practically deployable semantic inference theories and algorithms.

Topics of interest include (but are not limited to):

  • Theory and algorithms of robust, generic, and scalable semantic inference
  • Self-learning and interactive learning for online adaptable semantic inference
  • Exploration of applicability scope and theoretical performance limits of semantic inference algorithms
  • Modeling of system confidence in its semantic inference performance
  • Evaluation of semantic inference dependability using standard dependability criteria
  • Matching user/context requirements to dependability criteria (e.g., mobile user, user at home, etc.)
  • Modeling synergies between different semantic inference mechanisms (e.g., content analysis, indexing through user interaction, collaborative filtering)
  • Synergetic integration of content analysis, user actions (e.g., tagging, interaction with content) and user/device collaboration (e.g., in social/P2P networks)

Authors should follow the EURASIP Journal on Image and Video Processing manuscript format described at http://www.hindawi.com/journals/ivp/. Prospective authors should submit an electronic copy of their complete manuscripts through the journal Manuscript Tracking System at http://mts.hindawi.com/, according to the following timetable:

Manuscript DueDecember 1, 2009
First Round of ReviewsMarch 1, 2010
Publication DateJune 1, 2010

Guest Editors

  • Alan Hanjalic, Department of Mediamatics, Delft University of Technology, 2600 AA Delft, The Netherlands
  • Tat-Seng Chua, National University of Singapore, Singapore 119077
  • Edward Chang, Google Inc., China; Department of Electrical & Computer Engineering, University of California, Santa Barbara, CA 93106, USA
  • Ramesh Jain, Computer Science Department, Donald Bren School of Information & Computer Sciences, University of California, Irvine, CA 92697, USA