Multimedia information retrieval (MIR) deals with the development of effective an d efficient indexing and retrieval techniques for multimedia data. The recent exp losive growth of the use of multimedia information in many applications requires advanced techniques for multimedia information retrieval. However, it is well-kno wn that research in MIR has been thwarted by the growing semantics gap between th e high level semantics expected from human users and the low level features curre ntly used for indexing and retrieval of multimedia information in many existing c ontent based retrieval systems. Recent development in MIR has revealed that machi ne learning techniques can provide effective solutions to diminish this gap. Cons equently, many existing and new research issues in MIR can be effectively tackled by developing novel machine learning techniques. Such techniques can lead to the development of new tools that would substantially improve the retrieval efficien cy, effectiveness, and accessibility to multimedia information databases. For exa mple, recent research has demonstrated that machine learning techniques, with rel evance feedback, can provide substantial performance improvement for MIR.
The objective of this special issue is to address the current challenges and rese arch topics in using machine learning techniques to MIR.
Topics of interest include (but are not limited to):
Authors should follow the ACM Multimedia Systems journal manuscript format descri bed at the journal Website at http://cairo.cs.uiuc.edu/mmsj.html. Prospective aut hors should submit an electronic copy (PDF or Postscript) of their manuscript to the online submission system of the journal Website, according to the following t imetable:
Prof. Arif Ghafoor
School of Electrical and Computer Engineering
Prof. Zhongfei (Mark) Zhang
Computer Science Department
Prof. Michael S. Lew
LIACS Media Lab
Prof. Zhi-Hua Zhou
National Laboratory for Novel Software Technology