International Workshop on Semantic Knowledge in Computer Vision http://www.research.ge.com/vision/skcv05 October 16, 2005, Beijing, PRC. In association with ICCV 2005. Paper submission: July 18, 2005 (after ICCV decisions) Notification of acceptance: August 12 Receipt of camera ready copy: August 22 The use of semantic knowledge in computer vision is rapidly becoming more widespread and significant. In areas such as event recognition, object recognition and content-based image retrieval, context and common-sense knowledge are being used to achieve performance that is not attainable by purely bottom-up, data-driven approaches. In many applications, meaningful visual recognition is not possible without contextual, semantic support. However, the use of computational knowledge in computer vision is still in its infancy and many fundamental challenges remain. This workshop will bring together an interdisciplinary group of researchers in computer vision, knowledge representation and ontologies, machine learning, natural language and other areas to examine the issues and recent results in using semantic knowledge for vision problems. Recent progress in machine learning has enabled the rigorous management of uncertainty in large-scale reasoning problems, and this has re-kindled the use of semantic methods and reasoning in computer vision. Simultaneously, the natural language and artificial intelligence communities have developed large computational models and databases of semantic knowledge, such as CYC, OMCSNet and WordNet, that can be used for intelligent reasoning about real-world, common-sense knowledge. The multimedia and information fusion communities are using both evidential reasoning methods and semantic knowledgebases to fuse multiple data sources for intelligent object and event recognition. Papers are solicited in all disciplines related to the central theme, including but not limited to: o use of existing, large-scale knowledgebases/ontologies for vision problems o new ontologies for visual objects, video events, etc. o user-centric ontologies o unsupervised learning of event ontologies o automatic concept detection o semantic representations of spatio-temporal data o context-based recognition o high-level event recognition o semantic image and video annotation o semantic event-based retreival of video o content-based queries and use cases o integration of vision and natural language o visual learning vs. prior, structured knowledge o probabilistic models for dynamic systems o temporal logic in vision o multi-agent multi-threaded representations o situational awareness through visual perception o MPEG-7 PROGRAM The program will emphasize invited talks from researchers outside of CV, as well as those using high-level semantics to solve vision and perception problems. Approximately half of the program will consist of open submission papers. A follow-on workshop is planned for a related venue outside of computer vision, such as AAAI or IJCAI. ORGANIZATION General Chairs: Anthony Hoogs, GE Research hoogs@crd.ge.com Mubarak Shah, University of South Florida shah@cs.ucf.edu Tom Huang, University of Illinois at Urbana-Champaign huang@ifp.uiuc.edu Program Committee: Jake Aggarwal, University of Texas Kobus Barnard, University of Arizona Michael Chan, General Electric Rama Chellappa, University of Maryland John Kender, Columbia University Kevin Murphy, University of British Columbia Ram Nevatia, University of Southern California Jens Rittscher, General Electric Chris Town, Cambridge University James Wang, Pennsylvania State University PAPER SUBMISSION In keeping with the spirit of a workshop, submitted papers may emphasize intellectual risks and argue for ideas that do not yet have comprehensive experimental support. Hence papers may not need describe fully developed algorithms, methods, or results as would normally be required for acceptance at ICCV. Papers describing novel, unpublished research are solicited in the areas listed above and closely related topics. Reviewing will be double-blind by members of the program committee. Each paper will receive three reviews. Acceptance will be based on relevance to the workshop, novelty, and technical quality. Papers should be at most 8 pages in length, in the same style format as ICCV, and encoded as pdf. Please ftp your pdf file tousing the first author's last name as the filename (e.g. mylastname.pdf). One supplemental file may be included, up to a size of 10MB. Please send these via ftp also, using the same filename as the paper with "_supp" appended (e.g. mylastname_supp.pdf). All accepted papers will be included in the electronic ICCV proceedings. There will not be a hardcopy proceedings for this workshop.