Semantic Multimedia Computing aims to develop algorithms for enriching, accessing, and searching large quantities of data. Such algorithms lie at the core of tomorrows’ search engines and large-scale recommender systems. Thus, Semantic Multimedia Computing sets its focus on developing systems that are oriented to the needs of users, and that solve the challenges faced by large-scale online content and service providers. Multimedia data analytics has also applications in the full range of fields that benefit from data science, including health, telecom, entertainment, geosciences, etc.
As a result, Semantic Multimedia Computing deals with the development of technologies that make possible optimized interaction with large collections of multimedia data (e.g., images, video, and music) in real-world contexts (e.g., within social networks). That also requires a combination of mathematical models, machine learning techniques, and practical skills in algorithm development and evaluation.
This workshop aims at providing researchers and practitioners from different areas (multimedia information retrieval, recommender systems, multimedia signal processing, social network analysis, human computation) with an interdisciplinary forum to present, discuss, and exchange ideas that address the challenges of next-generation systems dealing with Semantic Multimedia Computing. The workshop seeks submissions from academia, government, and industry presenting novel research results in all practical and theoretical aspects of Semantic Multimedia Computing.
Topics of interest include, but are not limited to:
- Multimedia content analysis and search
- Semantics extraction from multimedia data.
- Multi-modal query expansion.
- Multi-source search result reranking.
- Multimedia information retrieval in a social network context
- Modeling information propagation and relationships in social networks.
- Collaborative recommender systems.
- Social recommendation.
- Interaction with multimedia content
- (Affective) User profiling.
- User (search/uploader) intent.
- Query failure prediction.
- Quality of multimedia experience.
- Multimedia content management
- Multimedia databases and dataspaces.
- Entity retrieval.