Semantic representation and analysis are among the most important branches of artificial intelligence, focusing on the description, measurement, and classification of patterns involved in multimedia data. In recent years, great progress has been made in both the theory and application of multimedia processing, and now a typical multimedia analysis system is composed of preprocessing, feature extraction, classifier design, and post-processing.

Cognition is emerging as a new and promising methodology in the development of cognitive-inspired computing and cognitive-inspired interactions and systems, which have the potential to have a substantial impact on our lives. However, recent advances in artificial intelligence (AI), fog computing, big data, and cognitive computational theory show that multidisciplinary cognitive-inspired computing still struggles with fundamental, long-standing problems, such as computational models and decision-making mechanisms based on the neurobiological processes of the brain, cognitive sciences, and psychology. The use of multimedia processing and applications to enhance human cognitive performance has great potential but requires new multimedia analysis theories to be adaptive to cognitive computational theory. It is therefore vital that new multimedia analysis applications are developed to benefit from cognitive computational theory.

The objective of this Special Issue is to bring together state-of-the-art research that addresses these key aspects of cognitive-inspired multimedia processing and related applications. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Cognitive-inspired computing fundamentals
• Cognitive-inspired computing systems
• Cognitive-inspired intelligent interaction
• AI-assisted cognitive computing approaches
• Brain analysis for cognitive-inspired computing
• Data representations based on machine learning techniques
• Image processing techniques to recover improved data representation
• Extraction of semantic representations for multimedia data representation
• Applications such as activity recognition, semantic multimedia summarization, multimedia captioning, action retrieval, and anomaly detection
• Data representation for egocentric multimedia analysis
• Machine learning architecture for pattern recognition
• Optimization for machine learning
• Machine learning for feature representation