Multimedia semantics

This theme investigates the design, construction, and application of ontologies and semantic models to capture the meanings embedded in multimedia data. Unlike traditional metadata or low-level descriptors, ontologies provide a formal, structured representation of multimedia semantics, enabling interoperability, advanced querying, and bridging the semantic gap between raw media features and human interpretation. The theme underscores why effective semantic modeling is fundamental to multimedia indexing, search, and content management in distributed and heterogeneous environments.

This area investigates models, algorithms, and computational frameworks that integrate heterogeneous modal data (e.g., audio, visual, textual) to extract unified semantic and affective information. Given user-generated content's multimodal nature, accurately deriving meaningful semantics and sentiments requires combining features across modalities and contextual metadata. Understanding effective multimodal fusion enhances multimedia summarization, tag relevance, personalized recommendation, and affective computing.

This research theme focuses on integrating domain knowledge, semantic reasoning, and contextual information to improve multimedia analysis tasks such as segmentation and classification. Traditional low-level feature-based segmentation and classification often face errors due to ambiguous boundaries or visually similar classes. Knowledge-driven approaches leverage semantic-level criteria, spatial and contextual relationships, and attribute-based classifiers to reduce these errors, enabling more accurate semantic labeling and retrieval of multimedia content.