Large amounts of multimedia material, such as images, audio, video, and
3D/4D material, as well as computer generated 2D, 3D, and 4D content,
already exist and are growing at increasing rates. While these amounts are
growing, managing distribution of and access to multimedia material is
becoming ever harder, both for lay and professional users.
The SAMT conference series tackles these problems by investigating the
semantics and pragmatics of multimedia generation, management, and user
access. The conference targets scientifically valuable research tackling the
semantic gap between the low-level signal data representation of
multimedia material and the high-level meaning that providers, consumers,
and prosumers associate with the content.
We welcome innovative solutions that consider some or all factors in the
process of multimedia generation and consumption, including methods
from low-level signal processing up to the mobile context in which a user
operates. Topics of interest include, but are not limited to:
SEMANTIC ANALYSIS AND MULTIMEDIA
- Knowledge assisted multimedia analysis
- Content-based multimedia analysis linked with natural language and
speech processing
- Semantic-driven multimedia indexing and retrieval
- Semantic retrieval of 3D objects
- Machine Learning and relevance feedback for finding semantics
- Semantic-driven multimedia content adaptation and summarization
- Metadata management for multimedia
- Multimedia ontologies and infrastructures
- Standards bridging the multimedia and knowledge domains
- Interfaces and personalization for interaction with large multimedia
repositories - Semantic media annotation
- Inference and machine learning for semi-automatic annotation
- Browsing multimedia archives
- Device-specific access to and adaptation of multimedia
- Illustrative depiction and rendering
- Mapping meaning to presentation content
- Smart virtual environments
- Supporting knowledge discovery
- Social multimedia tagging
- Context, user, network and semantics-aware media engineering
- Multimedia mash-ups
- Case studies with clear, innovative lessons learned
